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AI presents three major areas of ethical concern for society: privacy and surveillance, bias and discrimination, and perhaps the deepest, most difficult philosophical question of the era, the role of human judgment. Few think the federal government is up to the job, or will ever be.Requiring every new product using AI to be prescreened for potential social harms is not only impractical, but would create a huge drag on innovation.

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Illustration by Ben Boothman

Second in a four-part series that taps the expertise of the Harvard community to examine the promise and potential pitfalls of the rising age of artificial intelligence and machine learning, and how to humanize them.
For decades, artificial intelligence, or AI, was the engine of high-level STEM research. Most consumers became aware of the technology's power and potential through internet platforms like Google and Facebook, and retailer Amazon. Today, AI is essential across a vast array of industries, including health care, banking, retail, and manufacturing.

Also in the series

But its game-changing promise to do things like improve efficiency, bring down costs, and accelerate research and development has been tempered of late with worries that these complex, opaque systems may do more societal harm than economic good. With virtually no U.S. government oversight, private companies use AI software to make determinations about health and medicine, employment, creditworthiness, and even criminal justice without having to answer for how they're ensuring that programs aren't encoded, consciously or unconsciously, with structural biases.

Its growing appeal and utility are undeniable. Worldwide business spending on AI is expected to hit $50 billion this year and $110 billion annually by 2024, even after the global economic slump caused by the COVID-19 pandemic, according to a forecast released in August by technology research firm IDC. Retail and banking industries spent the most this year, at more than $5 billion each. The company expects the media industry and federal and central governments will invest most heavily between 2018 and 2023 and predicts that AI will be "the disrupting influence changing entire industries over the next decade."

"Virtually every big company now has multiple AI systems and counts the deployment of AI as integral to their strategy," said Joseph Fuller, professor of management practice at Harvard Business School, who co-leads Managing the Future of Work, a research project that studies, in part, the development and implementation of AI, including machine learning, robotics, sensors, and industrial automation, in business and the work world.

Early on, it was popularly assumed that the future of AI would involve the automation of simple repetitive tasks requiring low-level decision-making. But AI has rapidly grown in sophistication, owing to more powerful computers and the compilation of huge data sets. One branch, machine learning, notable for its ability to sort and analyze massive amounts of data and to learn over time, has transformed countless fields, including education.

Firms now use AI to manage sourcing of materials and products from suppliers and to integrate vast troves of information to aid in strategic decision-making, and because of its capacity to process data so quickly, AI tools are helping to minimize time in the pricey trial-and-error of product development - a critical advance for an industry like pharmaceuticals, where it costs $1 billion to bring a new pill to market, Fuller said.

Health care experts see many possible uses for AI, including with billing and processing necessary paperwork. And medical professionals expect that the biggest, most immediate impact will be in analysis of data, imaging, and diagnosis. Imagine, they say, having the ability to bring all of the medical knowledge available on a disease to any given treatment decision.

In employment, AI software culls and processes resumes and analyzes job interviewees' voice and facial expressions in hiring and driving the growth of what's known as "hybrid" jobs. Rather than replacing employees, AI takes on important technical tasks of their work, like routing for package delivery trucks, which potentially frees workers to focus on other responsibilities, making them more productive and therefore more valuable to employers.

"It's allowing them to do more stuff better, or to make fewer errors, or to capture their expertise and disseminate it more effectively in the organization," said Fuller, who has studied the effects and attitudes of workers who have lost or are likeliest to lose their jobs to AI.

"Can smart machines outthink us, or are certain elements of human judgment indispensable in deciding some of the most important things in life?"

- Michael Sandel, political philosopher and Anne T. and Robert M. Bass Professor of Government

Though automation is here to stay, the elimination of entire job categories, like highway toll-takers who were replaced by sensors because of AI's proliferation, is not likely, according to Fuller.

"What we're going to see is jobs that require human interaction, empathy, that require applying judgment to what the machine is creating [will] have robustness," he said.

While big business already has a huge head start, small businesses could also potentially be transformed by AI, says Karen Mills '75, M.B.A. '77, who ran the U.S. Small Business Administration from 2009 to 2013. With half the country employed by small businesses before the COVID-19 pandemic, that could have major implications for the national economy over the long haul.

Rather than hamper small businesses, the technology could give their owners detailed new insights into sales trends, cash flow, ordering, and other important financial information in real time so they can better understand how the business is doing and where problem areas might loom without having to hire anyone, become a financial expert, or spend hours laboring over the books every week, Mills said.

One area where AI could "completely change the game" is lending, where access to capital is difficult in part because banks often struggle to get an accurate picture of a small business's viability and creditworthiness.

"It's much harder to look inside a business operation and know what's going on" than it is to assess an individual, she said.

Information opacity makes the lending process laborious and expensive for both would-be borrowers and lenders, and applications are designed to analyze larger companies or those who've already borrowed, a built-in disadvantage for certain types of businesses and for historically underserved borrowers, like women and minority business owners, said Mills, a senior fellow at HBS.

But with AI-powered software pulling information from a business's bank account, taxes, and online bookkeeping records and comparing it with data from thousands of similar businesses, even small community banks will be able to make informed assessments in minutes, without the agony of paperwork and delays, and, like blind auditions for musicians, without fear that any inequity crept into the decision-making.

"All of that goes away," she said.

A veneer of objectivity

Not everyone sees blue skies on the horizon, however. Many worry whether the coming age of AI will bring new, faster, and frictionless ways to discriminate and divide at scale.

"Part of the appeal of algorithmic decision-making is that it seems to offer an objective way of overcoming human subjectivity, bias, and prejudice," said political philosopher Michael Sandel, Anne T. and Robert M. Bass Professor of Government. "But we are discovering that many of the algorithms that decide who should get parole, for example, or who should be presented with employment opportunities or housing ... replicate and embed the biases that already exist in our society."

"If we're not thoughtful and careful, we're going to end up with redlining again."

- Karen Mills, senior fellow at the Business School and head of the U.S. Small Business Administration from 2009 to 2013

AI presents three major areas of ethical concern for society: privacy and surveillance, bias and discrimination, and perhaps the deepest, most difficult philosophical question of the era, the role of human judgment, said Sandel, who teaches a course in the moral, social, and political implications of new technologies.

"Debates about privacy safeguards and about how to overcome bias in algorithmic decision-making in sentencing, parole, and employment practices are by now familiar," said Sandel, referring to conscious and unconscious prejudices of program developers and those built into datasets used to train the software. "But we've not yet wrapped our minds around the hardest question: Can smart machines outthink us, or are certain elements of human judgment indispensable in deciding some of the most important things in life?"

Panic over AI suddenly injecting bias into everyday life en masse is overstated, says Fuller. First, the business world and the workplace, rife with human decision-making, have always been riddled with "all sorts" of biases that prevent people from making deals or landing contracts and jobs.

When calibrated carefully and deployed thoughtfully, resume-screening software allows a wider pool of applicants to be considered than could be done otherwise, and should minimize the potential for favoritism that comes with human gatekeepers, Fuller said.

Sandel disagrees. "AI not only replicates human biases, it confers on these biases a kind of scientific credibility. It makes it seem that these predictions and judgments have an objective status," he said.

In the world of lending, algorithm-driven decisions do have a potential "dark side," Mills said. As machines learn from data sets they're fed, chances are "pretty high" they may replicate many of the banking industry's past failings that resulted in systematic disparate treatment of African Americans and other marginalized consumers.

"If we're not thoughtful and careful, we're going to end up with redlining again," she said.

A highly regulated industry, banks are legally on the hook if the algorithms they use to evaluate loan applications end up inappropriately discriminating against classes of consumers, so those "at the top levels" in the field are "very focused" right now on this issue, said Mills, who closely studies the rapid changes in financial technology, or "fintech."

"They really don't want to discriminate. They want to get access to capital to the most creditworthy borrowers," she said. "That's good business for them, too."

Oversight overwhelmed

Given its power and expected ubiquity, some argue that the use of AI should be tightly regulated. But there's little consensus on how that should be done and who should make the rules.

Thus far, companies that develop or use AI systems largely self-police, relying on existing laws and market forces, like negative reactions from consumers and shareholders or the demands of highly-prized AI technical talent to keep them in line.

"There's no businessperson on the planet at an enterprise of any size that isn't concerned about this and trying to reflect on what's going to be politically, legally, regulatorily, [or] ethically acceptable," said Fuller.

Firms already consider their own potential liability from misuse before a product launch, but it's not realistic to expect companies to anticipate and prevent every possible unintended consequence of their product, he said.

Few think the federal government is up to the job, or will ever be.

"The regulatory bodies are not equipped with the expertise in artificial intelligence to engage in [oversight] without some real focus and investment," said Fuller, noting the rapid rate of technological change means even the most informed legislators can't keep pace. Requiring every new product using AI to be prescreened for potential social harms is not only impractical, but would create a huge drag on innovation.

"I wouldn't have a central AI group that has a division that does cars, I would have the car people have a division of people who are really good at AI."

- Jason Furman, a professor of the practice of economic policy at the Kennedy School and a former top economic adviser to President Barack Obama

Jason Furman, a professor of the practice of economic policy at Harvard Kennedy School, agrees that government regulators need "a much better technical understanding of artificial intelligence to do that job well," but says they could do it.

Existing bodies like the National Highway Transportation Safety Association, which oversees vehicle safety, for example, could handle potential AI issues in autonomous vehicles rather than a single watchdog agency, he said.

"I wouldn't have a central AI group that has a division that does cars, I would have the car people have a division of people who are really good at AI," said Furman, a former top economic adviser to President Barack Obama.

Though keeping AI regulation within industries does leave open the possibility of co-opted enforcement, Furman said industry-specific panels would be far more knowledgeable about the overarching technology of which AI is simply one piece, making for more thorough oversight.

While the European Union already has rigorous data-privacy laws and the European Commission is considering a formal regulatory framework for ethical use of AI, the U.S. government has historically been late when it comes to tech regulation.

"I think we should've started three decades ago, but better late than never," said Furman, who thinks there needs to be a "greater sense of urgency" to make lawmakers act.

Business leaders "can't have it both ways," refusing responsibility for AI's harmful consequences while also fighting government oversight, Sandel maintains.

"The problem is these big tech companies are neither self-regulating, nor subject to adequate government regulation. I think there needs to be more of both," he said, later adding: "We can't assume that market forces by themselves will sort it out. That's a mistake, as we've seen with Facebook and other tech giants."

Last fall, Sandel taught " Tech Ethics," a popular new Gen Ed course with Doug Melton, co-director of Harvard's Stem Cell Institute. As in his legendary "Justice" course, students consider and debate the big questions about new technologies, everything from gene editing and robots to privacy and surveillance.

"Companies have to think seriously about the ethical dimensions of what they're doing and we, as democratic citizens, have to educate ourselves about tech and its social and ethical implications - not only to decide what the regulations should be, but also to decide what role we want big tech and social media to play in our lives," said Sandel.

Doing that will require a major educational intervention, both at Harvard and in higher education more broadly, he said.

"We have to enable all students to learn enough about tech and about the ethical implications of new technologies so that when they are running companies or when they are acting as democratic citizens, they will be able to ensure that technology serves human purposes rather than undermines a decent civic life."
Next: The AI revolution in medicine may lift personalized treatment, fill gaps in access to care, and cut red tape. Yet risks abound.
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AP Photo/Michael Dwyer

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Ethical concerns mount as AI takes bigger decision-making role

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Harvard experts examine the promise and potential pitfalls as AI takes a bigger decision-making role in more industries.

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There could be serious problems if the AI is not handled properly, especially in the fields of health care. "Therefore, it’s imperative AI research and development organizations proactively create safeguards against misuse and build in ethical checks and balances. Developing comprehensive ethics programs that evolve alongside innovations ensures AI-powered technologies are safe to use and publicly trusted."

# data[].attributes.attachment_link.readable_text
Has artificial intelligence outwitted us already? Not quite.

When the term "artificial intelligence" (AI) was first coined in the 1950s, the ultimate dream was to create a technology more intelligent than humans. Since then, AI research has experienced ups and downs. However, since the rise of machine learning - particularly deep learning - it seems AI is finally gathering momentum, with each success story followed by another.

Today, there are a variety of tasks AI systems can do better - and faster - than humans. Even creativity - an area long thought to be inaccessible to AI systems - is within reach. In 2017, the computer software AlphaGo Zero beat its predecessor at the Chinese board game "Go" by making a move that no human would ever have contemplated. The software was not trained with human data, but only with data from games played between computers. Entirely eliminating the need for human input was impressive in itself, but move 37 became a landmark in AI research because it proved an AI system could move beyond the data it was given as input, demonstrating its creative ability.

Are AI systems about to outperform humans?

Not quite. Humanity's strength lies elsewhere: We are fast learners and adapt quickly to new tasks. For AI systems, this is a real stumbling block. For example, to master a relatively simple computer game, which could take an average person 15 minutes to learn, AI systems need up to 924 hours. As for adaptability, if just one rule is altered, the AI system has to learn the entire game from scratch.

Another reason AI systems are still a bit risky is that when they are wrong, they are often completely wrong. Think of the viral YouTube video of a little boy asking Amazon's Alexa to play a popular children's song, which Alexa misinterpreted and instead offered to play an adult film. While no human would interpret the request this way, Alexa - and AI systems in general - still lack one of humanity's most underestimated skills:common sense.

In other words, to create the perfect digital assistant, we need to make AI systems more human-like. That does not mean we need to give robots self-awareness or make them look like humans. AI systems in the form of software embedded in a phone or glasses can be equally powerful. However, we do need AI systems that can think and communicate like humans.
Thinking Like A Human: Where Deep Learning Hits A Wall
The recent success in AI systems and machine learning can be attributed to the rise of deep learning, the most commonly used machine learning strategy. What makes deep learning powerful is its use of artificial neural networks, i.e., networks of digital neurons inspired by the human brain. Most AI breakthroughs in the last decade were enabled by this technique. Nevertheless, in the end, it is still just pattern recognition. They can map inputs to outputs but cannot reason about the decisions made. The learning and decision-making process is a black box, to us and to the AI itself.

So how do we develop artificial intelligence that can understand? The answer is to look back. Deep learning may have been the driving force behind recent breakthroughs, but to create an AI system that can think, we need a hybrid method, combining deep learning with older machine learning techniques with more transparent learning processes.
Lost In Context: Human-AI Interaction
Another issue that needs to be solved before AI systems can make their big breakthrough is communication. Although chatbots and digital assistants are fairly well established in commercial services and consumer applications, communicating with them can be challenging. We have come a long way since the first awkward encounter with the therapeutic chatbot, Eliza, but we still cannot have a completely natural conversation with an AI system. This challenge is harder to solve than it might seem, proven by the annual Alexa prize. Research teams compete to develop a chatbot with whom you can have a meaningful conversation for at least 20 minutes. In the last two editions, no one managed to earn the main prize, a $1 million research grant. Last year, the record was about nine and a half minutes.

One thing that makes AI systems struggle with real conversations is its ability to take context into account. If we want AI to become useful digital assistants, they need to be able to observe, analyze and understand the environment in order to provide real-time and proactive support. They also need to be able to interpret humans better: We do not always say what we really mean. The same phrase can have a completely different meaning when it's spoken in a different tone of voice.
When Human-Like AI Joins The Workforce
Although enormous progress has been made, we have only scratched the surface of what AI can do. With deep learning, we have established a solid foundation, but for human-like AI, we need additional building blocks, including rediscovering the potential of older machine learning strategies.

In 10 years' time, AI systems will become omnipresent in our workplaces. They will not replace human workers, but rather assist them. In many tasks, human employees will be supported by "cobots" - collaborative robots that can interact with their human colleagues in a spontaneous and natural way. By then, AI-colleagues will be able to adapt to new tasks more quickly without having to repeat their entire learning process. They will not be limited to robot-like machines either -- they will also be software-embedded.
The Most Important Consideration In AI: Ethics
As we move further into uncharted territory with machines, it's easy to imagine the serious problems that could occur if AI systems were not handled properly. Therefore, it's imperative AI research and development organizations proactively create safeguards against misuse and build in ethical checks and balances. Developing comprehensive ethics programs that evolve alongside innovations ensures AI-powered technologies are safe to use and publicly trusted.

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Council Post: Are AI Systems About To Outperform Humans?

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Has artificial intelligence outwitted us already? Not quite. When the term "artificial intelligence" (AI) was first coined in the 1950s, the ultimate dream was to create a technology more intelligent than humans. Since then, AI research has experienced ups and downs.

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When Human like AI join the workforce. Will it replace us humans? 

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When Human like AI join the workforce. Will it replace us humans? 

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AI systems will become omnipresent in our workplaces. They will not replace human workers, but rather assist them. Collaborative robots can interact with human colleague.

# data[].attributes.attachment_link.readable_text
Has artificial intelligence outwitted us already? Not quite.

When the term "artificial intelligence" (AI) was first coined in the 1950s, the ultimate dream was to create a technology more intelligent than humans. Since then, AI research has experienced ups and downs. However, since the rise of machine learning - particularly deep learning - it seems AI is finally gathering momentum, with each success story followed by another.

Today, there are a variety of tasks AI systems can do better - and faster - than humans. Even creativity - an area long thought to be inaccessible to AI systems - is within reach. In 2017, the computer software AlphaGo Zero beat its predecessor at the Chinese board game "Go" by making a move that no human would ever have contemplated. The software was not trained with human data, but only with data from games played between computers. Entirely eliminating the need for human input was impressive in itself, but move 37 became a landmark in AI research because it proved an AI system could move beyond the data it was given as input, demonstrating its creative ability.

Are AI systems about to outperform humans?

Not quite. Humanity's strength lies elsewhere: We are fast learners and adapt quickly to new tasks. For AI systems, this is a real stumbling block. For example, to master a relatively simple computer game, which could take an average person 15 minutes to learn, AI systems need up to 924 hours. As for adaptability, if just one rule is altered, the AI system has to learn the entire game from scratch.

Another reason AI systems are still a bit risky is that when they are wrong, they are often completely wrong. Think of the viral YouTube video of a little boy asking Amazon's Alexa to play a popular children's song, which Alexa misinterpreted and instead offered to play an adult film. While no human would interpret the request this way, Alexa - and AI systems in general - still lack one of humanity's most underestimated skills:common sense.

In other words, to create the perfect digital assistant, we need to make AI systems more human-like. That does not mean we need to give robots self-awareness or make them look like humans. AI systems in the form of software embedded in a phone or glasses can be equally powerful. However, we do need AI systems that can think and communicate like humans.
Thinking Like A Human: Where Deep Learning Hits A Wall
The recent success in AI systems and machine learning can be attributed to the rise of deep learning, the most commonly used machine learning strategy. What makes deep learning powerful is its use of artificial neural networks, i.e., networks of digital neurons inspired by the human brain. Most AI breakthroughs in the last decade were enabled by this technique. Nevertheless, in the end, it is still just pattern recognition. They can map inputs to outputs but cannot reason about the decisions made. The learning and decision-making process is a black box, to us and to the AI itself.

So how do we develop artificial intelligence that can understand? The answer is to look back. Deep learning may have been the driving force behind recent breakthroughs, but to create an AI system that can think, we need a hybrid method, combining deep learning with older machine learning techniques with more transparent learning processes.
Lost In Context: Human-AI Interaction
Another issue that needs to be solved before AI systems can make their big breakthrough is communication. Although chatbots and digital assistants are fairly well established in commercial services and consumer applications, communicating with them can be challenging. We have come a long way since the first awkward encounter with the therapeutic chatbot, Eliza, but we still cannot have a completely natural conversation with an AI system. This challenge is harder to solve than it might seem, proven by the annual Alexa prize. Research teams compete to develop a chatbot with whom you can have a meaningful conversation for at least 20 minutes. In the last two editions, no one managed to earn the main prize, a $1 million research grant. Last year, the record was about nine and a half minutes.

One thing that makes AI systems struggle with real conversations is its ability to take context into account. If we want AI to become useful digital assistants, they need to be able to observe, analyze and understand the environment in order to provide real-time and proactive support. They also need to be able to interpret humans better: We do not always say what we really mean. The same phrase can have a completely different meaning when it's spoken in a different tone of voice.
When Human-Like AI Joins The Workforce
Although enormous progress has been made, we have only scratched the surface of what AI can do. With deep learning, we have established a solid foundation, but for human-like AI, we need additional building blocks, including rediscovering the potential of older machine learning strategies.

In 10 years' time, AI systems will become omnipresent in our workplaces. They will not replace human workers, but rather assist them. In many tasks, human employees will be supported by "cobots" - collaborative robots that can interact with their human colleagues in a spontaneous and natural way. By then, AI-colleagues will be able to adapt to new tasks more quickly without having to repeat their entire learning process. They will not be limited to robot-like machines either -- they will also be software-embedded.
The Most Important Consideration In AI: Ethics
As we move further into uncharted territory with machines, it's easy to imagine the serious problems that could occur if AI systems were not handled properly. Therefore, it's imperative AI research and development organizations proactively create safeguards against misuse and build in ethical checks and balances. Developing comprehensive ethics programs that evolve alongside innovations ensures AI-powered technologies are safe to use and publicly trusted.

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Council Post: Are AI Systems About To Outperform Humans?

# data[].attributes.attachment_link.description
Has artificial intelligence outwitted us already? Not quite. When the term "artificial intelligence" (AI) was first coined in the 1950s, the ultimate dream was to create a technology more intelligent than humans. Since then, AI research has experienced ups and downs.

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AI systems struggle with real conversation. It struggle with the ability to take context into account. If we want AI to become useful digital assistants, they need to be able to observe analyze and understand the environment. They also need to interpret humans betters.

# data[].attributes.attachment_link.readable_text
Has artificial intelligence outwitted us already? Not quite.

When the term "artificial intelligence" (AI) was first coined in the 1950s, the ultimate dream was to create a technology more intelligent than humans. Since then, AI research has experienced ups and downs. However, since the rise of machine learning - particularly deep learning - it seems AI is finally gathering momentum, with each success story followed by another.

Today, there are a variety of tasks AI systems can do better - and faster - than humans. Even creativity - an area long thought to be inaccessible to AI systems - is within reach. In 2017, the computer software AlphaGo Zero beat its predecessor at the Chinese board game "Go" by making a move that no human would ever have contemplated. The software was not trained with human data, but only with data from games played between computers. Entirely eliminating the need for human input was impressive in itself, but move 37 became a landmark in AI research because it proved an AI system could move beyond the data it was given as input, demonstrating its creative ability.

Are AI systems about to outperform humans?

Not quite. Humanity's strength lies elsewhere: We are fast learners and adapt quickly to new tasks. For AI systems, this is a real stumbling block. For example, to master a relatively simple computer game, which could take an average person 15 minutes to learn, AI systems need up to 924 hours. As for adaptability, if just one rule is altered, the AI system has to learn the entire game from scratch.

Another reason AI systems are still a bit risky is that when they are wrong, they are often completely wrong. Think of the viral YouTube video of a little boy asking Amazon's Alexa to play a popular children's song, which Alexa misinterpreted and instead offered to play an adult film. While no human would interpret the request this way, Alexa - and AI systems in general - still lack one of humanity's most underestimated skills:common sense.

In other words, to create the perfect digital assistant, we need to make AI systems more human-like. That does not mean we need to give robots self-awareness or make them look like humans. AI systems in the form of software embedded in a phone or glasses can be equally powerful. However, we do need AI systems that can think and communicate like humans.
Thinking Like A Human: Where Deep Learning Hits A Wall
The recent success in AI systems and machine learning can be attributed to the rise of deep learning, the most commonly used machine learning strategy. What makes deep learning powerful is its use of artificial neural networks, i.e., networks of digital neurons inspired by the human brain. Most AI breakthroughs in the last decade were enabled by this technique. Nevertheless, in the end, it is still just pattern recognition. They can map inputs to outputs but cannot reason about the decisions made. The learning and decision-making process is a black box, to us and to the AI itself.

So how do we develop artificial intelligence that can understand? The answer is to look back. Deep learning may have been the driving force behind recent breakthroughs, but to create an AI system that can think, we need a hybrid method, combining deep learning with older machine learning techniques with more transparent learning processes.
Lost In Context: Human-AI Interaction
Another issue that needs to be solved before AI systems can make their big breakthrough is communication. Although chatbots and digital assistants are fairly well established in commercial services and consumer applications, communicating with them can be challenging. We have come a long way since the first awkward encounter with the therapeutic chatbot, Eliza, but we still cannot have a completely natural conversation with an AI system. This challenge is harder to solve than it might seem, proven by the annual Alexa prize. Research teams compete to develop a chatbot with whom you can have a meaningful conversation for at least 20 minutes. In the last two editions, no one managed to earn the main prize, a $1 million research grant. Last year, the record was about nine and a half minutes.

One thing that makes AI systems struggle with real conversations is its ability to take context into account. If we want AI to become useful digital assistants, they need to be able to observe, analyze and understand the environment in order to provide real-time and proactive support. They also need to be able to interpret humans better: We do not always say what we really mean. The same phrase can have a completely different meaning when it's spoken in a different tone of voice.
When Human-Like AI Joins The Workforce
Although enormous progress has been made, we have only scratched the surface of what AI can do. With deep learning, we have established a solid foundation, but for human-like AI, we need additional building blocks, including rediscovering the potential of older machine learning strategies.

In 10 years' time, AI systems will become omnipresent in our workplaces. They will not replace human workers, but rather assist them. In many tasks, human employees will be supported by "cobots" - collaborative robots that can interact with their human colleagues in a spontaneous and natural way. By then, AI-colleagues will be able to adapt to new tasks more quickly without having to repeat their entire learning process. They will not be limited to robot-like machines either -- they will also be software-embedded.
The Most Important Consideration In AI: Ethics
As we move further into uncharted territory with machines, it's easy to imagine the serious problems that could occur if AI systems were not handled properly. Therefore, it's imperative AI research and development organizations proactively create safeguards against misuse and build in ethical checks and balances. Developing comprehensive ethics programs that evolve alongside innovations ensures AI-powered technologies are safe to use and publicly trusted.

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Council Post: Are AI Systems About To Outperform Humans?

# data[].attributes.attachment_link.description
Has artificial intelligence outwitted us already? Not quite. When the term "artificial intelligence" (AI) was first coined in the 1950s, the ultimate dream was to create a technology more intelligent than humans. Since then, AI research has experienced ups and downs.

# data[].attributes.headline
"Because at the end of the day, the most valuable and non-replaceable thing is human-human interaction." 

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"Because at the end of the day, the most valuable and non-replaceable thing is human-human interaction." 

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(Bhattacharya S, Pradhan KB, Bashar MA, Tripathi S, Semwal J, Marzo RR, et. al)

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The healthcare industry takes care of the sick, injured, disabled remained a human support for a long time. It is difficult now to comment upon the complete replacement of emotional component of humans.   (Bhattacharya S, Pradhan KB, Bashar MA, Tripathi S, Semwal J, Marzo RR, et. al)

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possibility of losing individual clinical judgment/creativity. A traditional doctor makes a wrong diagnosis, he may kill that single patient. If all AI-enabled doctors belong to a single system, the system makes a mistake, the results may be devastating. (Bhattacharya S, Pradhan KB, Bashar MA, Tripathi S, Semwal J, Marzo RR, et. al)

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AI's competitive advantage over unique non-human abilities

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AI's competitive advantage over unique non-human abilities

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practical advantage : easy and quick connectivity and updatability. To update the medical knowledge of all human doctors, it will be quite a time-consuming exercise. It is not possible to connect them and update them in real time if any pandemic occurs. During times of new infection, the information can be shared around AI-enbaled doctors with in a second. (Bhattacharya S, Pradhan KB, Bashar MA, Tripathi S, Semwal J, Marzo RR, et. al)

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NO. AI systems are not close to outperform humans

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NO. AI systems are not close to outperform humans

# data[].attributes.body
Humans are fast learners. To master a relatively simple computer game, which could take an average person 15 minutes learn, AI systems need up to 924 hours.

# data[].attributes.attachment_link.readable_text
Has artificial intelligence outwitted us already? Not quite.

When the term "artificial intelligence" (AI) was first coined in the 1950s, the ultimate dream was to create a technology more intelligent than humans. Since then, AI research has experienced ups and downs. However, since the rise of machine learning - particularly deep learning - it seems AI is finally gathering momentum, with each success story followed by another.

Today, there are a variety of tasks AI systems can do better - and faster - than humans. Even creativity - an area long thought to be inaccessible to AI systems - is within reach. In 2017, the computer software AlphaGo Zero beat its predecessor at the Chinese board game "Go" by making a move that no human would ever have contemplated. The software was not trained with human data, but only with data from games played between computers. Entirely eliminating the need for human input was impressive in itself, but move 37 became a landmark in AI research because it proved an AI system could move beyond the data it was given as input, demonstrating its creative ability.

Are AI systems about to outperform humans?

Not quite. Humanity's strength lies elsewhere: We are fast learners and adapt quickly to new tasks. For AI systems, this is a real stumbling block. For example, to master a relatively simple computer game, which could take an average person 15 minutes to learn, AI systems need up to 924 hours. As for adaptability, if just one rule is altered, the AI system has to learn the entire game from scratch.

Another reason AI systems are still a bit risky is that when they are wrong, they are often completely wrong. Think of the viral YouTube video of a little boy asking Amazon's Alexa to play a popular children's song, which Alexa misinterpreted and instead offered to play an adult film. While no human would interpret the request this way, Alexa - and AI systems in general - still lack one of humanity's most underestimated skills:common sense.

In other words, to create the perfect digital assistant, we need to make AI systems more human-like. That does not mean we need to give robots self-awareness or make them look like humans. AI systems in the form of software embedded in a phone or glasses can be equally powerful. However, we do need AI systems that can think and communicate like humans.
Thinking Like A Human: Where Deep Learning Hits A Wall
The recent success in AI systems and machine learning can be attributed to the rise of deep learning, the most commonly used machine learning strategy. What makes deep learning powerful is its use of artificial neural networks, i.e., networks of digital neurons inspired by the human brain. Most AI breakthroughs in the last decade were enabled by this technique. Nevertheless, in the end, it is still just pattern recognition. They can map inputs to outputs but cannot reason about the decisions made. The learning and decision-making process is a black box, to us and to the AI itself.

So how do we develop artificial intelligence that can understand? The answer is to look back. Deep learning may have been the driving force behind recent breakthroughs, but to create an AI system that can think, we need a hybrid method, combining deep learning with older machine learning techniques with more transparent learning processes.
Lost In Context: Human-AI Interaction
Another issue that needs to be solved before AI systems can make their big breakthrough is communication. Although chatbots and digital assistants are fairly well established in commercial services and consumer applications, communicating with them can be challenging. We have come a long way since the first awkward encounter with the therapeutic chatbot, Eliza, but we still cannot have a completely natural conversation with an AI system. This challenge is harder to solve than it might seem, proven by the annual Alexa prize. Research teams compete to develop a chatbot with whom you can have a meaningful conversation for at least 20 minutes. In the last two editions, no one managed to earn the main prize, a $1 million research grant. Last year, the record was about nine and a half minutes.

One thing that makes AI systems struggle with real conversations is its ability to take context into account. If we want AI to become useful digital assistants, they need to be able to observe, analyze and understand the environment in order to provide real-time and proactive support. They also need to be able to interpret humans better: We do not always say what we really mean. The same phrase can have a completely different meaning when it's spoken in a different tone of voice.
When Human-Like AI Joins The Workforce
Although enormous progress has been made, we have only scratched the surface of what AI can do. With deep learning, we have established a solid foundation, but for human-like AI, we need additional building blocks, including rediscovering the potential of older machine learning strategies.

In 10 years' time, AI systems will become omnipresent in our workplaces. They will not replace human workers, but rather assist them. In many tasks, human employees will be supported by "cobots" - collaborative robots that can interact with their human colleagues in a spontaneous and natural way. By then, AI-colleagues will be able to adapt to new tasks more quickly without having to repeat their entire learning process. They will not be limited to robot-like machines either -- they will also be software-embedded.
The Most Important Consideration In AI: Ethics
As we move further into uncharted territory with machines, it's easy to imagine the serious problems that could occur if AI systems were not handled properly. Therefore, it's imperative AI research and development organizations proactively create safeguards against misuse and build in ethical checks and balances. Developing comprehensive ethics programs that evolve alongside innovations ensures AI-powered technologies are safe to use and publicly trusted.

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Council Post: Are AI Systems About To Outperform Humans?

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Has artificial intelligence outwitted us already? Not quite. When the term "artificial intelligence" (AI) was first coined in the 1950s, the ultimate dream was to create a technology more intelligent than humans. Since then, AI research has experienced ups and downs.

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From the research, the authors believed AI-enabled doctors can outperform human doctors in the cognitive domain

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From the research, the authors believed AI-enabled doctors can outperform human doctors in the cognitive domain

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This is why good surgeons, clinicians sometimes can make misdiagnosis and wrong move on the patients. Diagnosing a disease by a doctor is not dependent upon intuitions. Rather, their brains spot biochemical patterns by evaluating signs and symptoms of the patients. If AI equipped with the right sensors and software, it could treat patients more accurately. The AI field hold promise to fill the gap of human doctors in the resrouce poor areas. (Bhattacharya S, Pradhan KB, Bashar MA, Tripathi S, Semwal J, Marzo RR, et. al)

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The research findings highlighted that our all choices/decisions/desires resulted not from some enigmatic intuitions, but were due to the probabilities calculated by billions of neruons within a fraction of second. Overhyped human intuition is just pattern/event recognizion. (Bhattacharya S, Pradhan KB, Bashar MA, Tripathi S, Semwal J, Marzo RR, et. al)

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"The better we understand the biochemical mechanisms of human emotions, desires, and choices, the better computers can become in analyzing and predicting human behaviors, and hence in replacing human drivers, bankers and lawyers." 

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"The better we understand the biochemical mechanisms of human emotions, desires, and choices, the better computers can become in analyzing and predicting human behaviors, and hence in replacing human drivers, bankers and lawyers." 

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(Bhattacharya S, Pradhan KB, Bashar MA, Tripathi S, Semwal J, Marzo RR, et. al)

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Cognitive and physical aspects of human capabilities

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Cognitive and physical aspects of human capabilities

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Two domains of human capabilities. cognitive: learning, analyzing, communication and understanding human emotions. physical: manual jobs. AI revolution also includes breakthroughs in biological science. (Bhattacharya S, Pradhan KB, Bashar MA, Tripathi S, Semwal J, Marzo RR, et. al)

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The main disadvantage of RATS is the higher costs of instrumentation.

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Abstract

The robotic surgical system is the result of a long process of development aimed at producing a natural extension of the surgeon's eyes and hands via the intermediation of a computer. In this way, the ease of movement obtained with open surgery is summated with the advantages of the minimally invasive technique. Since 2000, when the first robotic system for surgery was introduced, robot-assisted thoracic surgery (RATS) has been adopted by an increasing number of centres around the world, and today is used in ∼10% of lobectomies in the US. Here, we review the characteristics and function of the robotic system available today (namely, Intuitive Surgical Inc.'s da Vinci Surgical System), outline the different techniques for major lung resection via RATS, compare RATS with video-assisted thoracoscopic surgery (VATS) and thoracotomy, and speculate on future developments. To date, no randomized trials have reported comparative data on RATS vs. VATS/thoracotomy for lung cancer. Retrospective analysis comparing RATS vs. thoracotomy have revealed advantages for the former, especially shorter hospital stays and a lower complication rate, but RATS produces similar or only slightly better results to VATS, the two being minimally invasive techniques with no need for rib separation. A few studies have reported RATS to be safer than VATS, with less conversions for bleeding, less complications; in others, it was associated with lower postoperative consumption of pain killers and quicker return of patients to normal activity. In addition, lymphnode upstaging has been shown to be higher with RATS than with VATS, with a similar rate as thoracotomy. The main disadvantage of RATS is the higher costs of instrumentation. Nevertheless, the future will probably see reductions in the costs and improvements in the instrumentation, integration with 3D imaging to improve virtual reality, and more patients benefitting from minimally invasive procedures for lung malignancies.

Introduction

When it was introduced in the operating room in 2002, the robot-assisted approach to pulmonary resection [1], [2] was considered by many thoracic surgeons a tool for privileged hospitals, on account of its high costs. Many limitations were emphasized, such as the spatial footprint of the apparatus, the complexity in installing the robot's arms into the patient's chest and the increased duration of surgery; operating at a distance from the patient was also considered a source of anxiety by many surgeons. As a result, time was needed to gain confidence with the new apparatus. Moreover, a change in surgeons' mentality was necessary before the new procedure could be accepted and embraced. Indeed, with time, surgeons increased their familiarity with the technique, the technology improved and most of the aforementioned limitations were overcome; in the meanwhile, advantages related to improved vision over the operative field, increased comfort for the surgeon and the precision of the manipulations became progressively more appreciated.

Indeed, according to the database of the Agency for Healthcare Research and Quality (AHRQ, http://hcupnet.ahrq.gov ) - a US government database that collates data from small hospitals and non-academia - in 2009, 66% of lobectomies were performed via thoracotomy, 33% via video-assisted thoracoscopic surgery (VATS) and only 1% with a robotic system; In 2013, the figures were 56%, 33% and 11%, respectively. Thus, robot-assisted thoracic surgery (RATS) was being increasingly used to the detriment of open surgery, with the proportion of VATS remaining stable. The data from the highly academic Society of Thoracic Surgeons' (STS) database showed a higher growth rate for VATS compared with that obtained from the AHRQ database, indicating that academic centers were performing lobectomies more with VATS. This was confirmed by Paul et al. [3]: analysis of the lobectomies deposited in the Nationwide Inpatient Sample (NIS) database - the largest all-payer inpatient healthcare database in the US - the absolute number of RATS lobectomies, the number of centers performing RATS lobectomy and the rate of RATS vs. all lobectomies per center increased dramatically between 2008 and 2011. Between 2011 and the end of 2015, the centers performing robot-assisted lobectomies increased 90% worldwide, with the number of procedures conducted with this technology increasing 185%. By the end of 2015, market analysis reports indicated that the share of robot-assisted lobectomies in the US was ∼15%, with over 400 surgeons performing RATS. There is a feeling that robotic systems are destined to become an essential reality in the surgeon's armamentarium of the future. Here, we review the available literature on robot-assisted thoracic procedures. We analyse the evidence obtained so far in terms of comparison of RATS with manual VATS and the open-surgery approach, and describe the different surgical techniques and the current limitations of the procedure. Moreover, we imagine perspectives for the future and the possible applications of this new technology.

Section snippets

Description of robotic technology

The da Vinci Surgical System (Intuitive Surgical Inc., Sunnyvale, CA, USA) is the only robot available on the market for RATS. It is a tele-surgical system that consists of a robotic unit with 3/4 arms (the slaves) controlled by the surgeon via a remote console (the master). The da Vinci robot is the result of a long developmental process aimed at naturally extending the surgeon's eyes and hands, and combines the ease of motion capable in open surgery with all the advantages of a minimally

Robot-assisted techniques for thoracic surgery

Three main techniques of RATS for lobectomy and segmentectomy have been described. The first - introduced by Park in 2006 - reproduces the patient and port positions of the anterior VATS approach with a utility incision of 3-4 cm in the IV intercostal space on the mid axillary line (Fig. 1), and uses two more trocars for the camera port and for the second instrument; as a result no CO2 insufflation is necessary [5], and the surgical steps reproduce those of VATS lobectomy, with

Training and learning curves for robotic assisted technique

The process of gaining experience and improving skills in surgery is called the "learning curve" [13]. Many studies confirm that robot-assisted surgeons may not need prior experience with laparoscopic or thoracoscopic surgery to adapt to the system. Hernandez et al. [14] observed that the learning process with Intuitive Surgical's da Vinci system (Sunnyvale, CA, USA) - the only available surgical robot on the market today - is shorter than that for laparoscopic surgery. The difference between

Comparison of robot-assisted with "open" major lung resections

Up until the last couple of decades, thoracotomy was the gold standard for the treatment of early stage lung cancer [18]. During the 1990s and 2000s, surgical technique has been refined and the global trend has led to MIS techniques [19]. Currently, the robot-assisted approach represents the most promising MIS option. However, even the most experienced minimally invasive surgeon cannot sidestep traditional open surgery. As a matter of fact, the treatment of locally advanced lung cancer

Comparison of robot-assisted with VATS major lung resections

Analysing the literature on pubmed, we selected eleven articles. No randomised studies are available while retrospective and case control analysis presented controversial results on benefits and limits of robotic approach compared to VATS.

Louie et al. [28] compared 52 robotic lobectomies and 32 vats lobectomies for early stage lung cancer and found a benefit in the reduction of pain, and hence of analgesic-drug use, and in an earlier return to usual activities; however, clinical outcomes were

Cost analysis and sustainability

The main argument against RATS over VATS is increased cost. Park et al. [40] analysed relative costs of thoracotomy (n = 267), VATS (n = 87) and RATS (n = 12). Operative room times were similar; postoperative stay was 4 days with VATS and RATS vs. 6 days with open surgery; and RATS was associated with an increased cost of US$3981 over VATS, incurred almost fully on the first day. Direct costs of specialized instruments and certain disposables was US$730. However, RATS was less costly than thoracotomy

Perspectives and new frontiers

The use of the robot is constantly growing throughout the world [34]. With the spread of robotic systems amongst health facilities, the number of procedures performed with this technology is growing exponentially. Robotic technology has made enormous strides to date, but in the near future we expect improvements beyond the actual use of robot-assisted surgery.

Thoracoscopic surgery is undergoing a rapid increase in the development and use of uniportal techniques [44], [45]. Robot-assisted

Conclusions

Robot-assisted technology has indisputable technical advantages over traditional video-assisted surgery. The robotic approach combines benefits of minimally invasive surgery with precision of movements, comfort and three-dimensional views of the surgical visual field. The learning curve for robot-assisted thoracic surgery necessitates work on 20 cases, but improvements continue up to the completion of 90 cases.

According to recent studies, for early stage non-small-cell lung carcinoma,

Conflict of interest

Giulia Veronesi is consultant for Abi medica. For other authors there are no conflicts of interest to declare.

Acknowledgements

The authors acknowledge support from FUV (Umberto Veronesi Foundation) for the fellowships of one staff surgeon and of the data manager.

We acknowledge the support of Michael Latronico and James Hughes for the English language review.

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Robot-assisted surgery for lung cancer: State of the art and perspectives

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The robotic surgical system is the result of a long process of development aimed at producing a natural extension of the surgeon's eyes and hands via the intermediation of a computer. In this way, the ease of movement obtained with open surgery is summated with the advantages of the minimally invasive technique.

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Example 4 of existence of AI in Health Care. Robot assisted surgery

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Example 4 of existence of AI in Health Care. Robot assisted surgery

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Robot assisted surgery is gaining popularity. Surgical bots are capable of doing complex minimal invasive procedures with better visualization, increase precision, and enhanced dexterity compared to laparoscopy. (Bhattacharya S, Pradhan KB, Bashar MA, Tripathi S, Semwal J, Marzo RR, et. al)

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Example 3 of existence of AI in Health Care "Niramai" diagnose breat cancer

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Example 3 of existence of AI in Health Care "Niramai" diagnose breat cancer

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This 'Thermalytix' technology can detect tumors five years earlier than mammography or clinical examples. No touch technique is required. The machine is portable and the costing is 1/10 of routine mammography of clinical exams. The skills set required for operating this machine is not very high. Already screened 4200 women and detected caner early on in some of them. The privacy component played a significant role, the test is carrier out with complete privacy. Particularly for rural women. (Bhattacharya S, Pradhan KB, Bashar MA, Tripathi S, Semwal J, Marzo RR, et. al)

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"20 % of the patients can be misdiagnosed by human doctors"

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Abstract

Background

To analyze the most common neurophthalmological conditions that may mimic glaucomatous optic neuropathy and to determine which most often lead to misdiagnosis when evaluated by a glaucoma specialist.

Methods

We reviewed the charts of consecutive patients with optic neuropathies caused by neurophthalmological conditions screened in a single Eye Clinic within a period of 24 months. Within these enrolled patients, we selected the eyes whose fundoscopic appearance could resemble glaucoma based in pre-defined criteria (vertical cup-to-disc ratio ≥0.6, asymmetry of the cup-to-disc ratio ≥0.2 between eyes, presence of localized retinal nerve fiber layer and/or neuroretinal rim defects, and disc haemorrhages). Then, color fundus photographs and Humphrey Visual Field tests (HVF) of these eyes were mixed with tests from 21 consecutive glaucomatous patients (42 eyes with normal tension glaucoma). These images were mixed randomly and a masked glaucoma specialist was asked to distinguish if each set of exams was from a patient with glaucoma or with a neurophthalmologic condition.

Results

Among the 101 eyes (68 patients) enrolled with neurophthalmological diseases, 16 (15.8%) were classified as conditions that could mimic glaucoma. The most common diagnoses were ischemic optic neuropathy (25%), compressive optic neuropathy (18.7%) and hereditary optic neuropathy (18.7%). Based on the analysis of fundus photographs and HVF tests, 25% of these were misdiagnosed as glaucoma (two ischemic optic neuropathies and two congenital optic disc anomalies). Conversely, 11.9% of the glaucomatous neuropathies were misdiagnosed as neurophthalmological disorders. Overall, the glaucoma specialist correctly diagnosed 84.5% of the eyes.

Conclusions

Some neurophthalmological disorders can mimic glaucoma. In our study, isquemic and compressive optic neuropathies were the ones that most often did so. Almost one quarter of the eyes were misdiagnosed when evaluated by a glaucoma specialist, which can lead to inadequate management and influence the prognosis of these patients.

Keywords: Optic disc, Neuro-ophthalmology: diagnosis, Intraocular pressure

Background

Glaucoma is characterized by retinal ganglion cell degeneration, alterations in optic nerve head topography, and associated visual field (VF) loss. Although elevated intraocular pressure (IOP) remains the most important known risk factor for the development and progression of glaucomatous optic neuropathy, a significant proportion of the cases may present with IOPs in the normal range [ 1- 6].

Since IOP is within the normal range in eyes with normal-tension glaucoma (NTG), a definitive diagnosis is not always straightforward in these cases, and it's important to consider all the differential diagnoses. While having in mind other forms of glaucoma, it is necessary to exclude cases of primary open angle glaucoma (POAG) with wide IOP fluctuations, steroid-induced glaucoma, cases of intermittent IOP increase (e.g. uveitis), pigmentary glaucoma (in older people) and others [ 7]. When thinking about forms of non-glaucomatous neuropathy, one should consider especially those that may present with optic disc cupping (besides visual field loss), such as anterior ischemic optic neuropathies (AION) (Fig. 1 ), hereditary optic neuropathies (Fig. 2 ), those associated with compressive lesions (Fig. 3 ) and demyelinating optic neuritis.

To distinguish glaucomatous and non-glaucomatous disc cupping can be challenging, especially in eyes with IOP within the normal range. In this context, some studies have tried to determine possible discriminating parameters to add clinicians in this task. These studies often focus on clinical data gathered from patients with one specific subgroup of neurophthalmological conditions, like compressive neuropathies, for example [ 8], or on the need for neurological assessment and neuroimaging in these cases, which remains controversial [ 9]. Nevertheless, there are scant data when it comes to neuropththalmological diseases in general that resembles glaucoma on daily practice, and especially those that can most often be misdiagnosed as NTG. Since the knowledge of this information could assist handling these challenging cases, we sought to investigate the most common neurophthalmological conditions that may mimic NTG and to determine which most often lead to misdiagnosis when evaluated by a glaucoma specialist.

Methods

This cross-sectional protocol adhered to the tenets of the Declaration of Helsinki and was approved by the Institutional Review Board. In addition, written informed consent was obtained from all participants.

Patients

We reviewed the charts of consecutive patients with optic neuropathies caused by neurophthalmological conditions screened in a single Eye Clinic within a period of 24 months. A consecutive set of patients with NTG followed at the same institution was included. The diagnosis of nonglaucomatous optic neuropathy was made by a single experienced neurophthalmologist (RB) based on clinical examination and ancillary exams, such as perimetry, retinography and neuroimaging tests. The diagnosis of NTG was made by a single glaucoma specialist (TSP) based on clinical examination and the presence of signs of glaucomatous optic neuropathy (GON) and characteristic VF loss. All included patients had undergone a comprehensive ophthalmological examination at baseline and performed ancillary exams according to the specialist's discretion. Exclusion criteria were significant media opacity (precluding proper fundus examination) and the presence of any other ophthalmological condition that could affect the optic nerve or the VF. All included eyes had a maximum untreated IOP <21 mmHg on at least two separate occasions.

Signs of GON were defined as vertical cup-to-disc ratio ≥0.6, asymmetry of the cup-to-disc ratio ≥0.2 between eyes, presence of localized retinal nerve fiber layer and/or neuroretinal rim defects, and disc haemorrhages. Characteristic glaucomatous VF loss was defined as three or more points in clusters with a probability of <5% (excluding those on the edge of the field or directly above and below the blind spot) on the pattern deviation plot, a pattern standard deviation index with a probability of <5%, or a glaucoma hemifield test with results outside the normal limits. Indices for VF test reliability were set at fixation loss <20%, false-negative <33% and false-positive <15%.

The color fundus photographs of all patients with stablished nonglaucomatous optic neuropathy diagnosis were simultaneously evaluated by two glaucoma specialists for identification of those whose fundoscopic appearance resembled glaucoma was based on signs suggestive of GON (described above). In cases of disagreement, the opinion of a third examiner was used for adjudication.

Data analysis and main outcome measures

After the patients' diagnoses had been stablished by the specialists and the cases of nonglaucomatous optic neuropathy that resembled glaucoma had been selected by the glaucoma specialists, color fundus photographs (Midriatic retinography; Visucam Lite, Carl Zeiss Meditec; AG07740, Jena, Germany) and standard achromatic perimetry tests results (Humphrey Visual Field Analyzer, Sita Standard, 24-2; Carl Zeiss Meditec; Dublin, CA) of the nonglaucomatous eyes were mixed randomly with those from the NTG patients and reviewed by a different glaucoma specialist (SD) in a masked fashion. The observer was asked to determine whether each subset of images was glaucoma or a nonglaucomatous optic neuropathy case based only on these data (retinography and VF test results). No other demographic or clinical characteristic (such as age, race or IOP) was provided for the examiner. Main outcome measures were: (1) identification of the neurophthalmological conditions that could mimic glaucoma based on the fundus aspect; (2) identification of the conditions that most often led to misdiagnosis (based on the nonglaucomatous optic neuropathy cases that were incorrectly classified as glaucoma); (3) quantification of the ability of the glaucoma specialist to discriminate between glaucomatous and nonglaucomatous eyes (based on the percentage of correct answers).

Results

We enrolled 101 eyes with nonglaucomatous optic neuropathy from 68 consecutive patients and 42 eyes with NTG from 21 patients. When comparing demographic characteristics between groups, we found NTG patients to be significantly older and with a higher prevalence of Asian descendants ( p < 0.01). Regarding ocular findings, NTG patients had lower IOP values on average ( p < 0.01). It is important to emphasize that all glaucomatous patients were under medical treatment. In addition, when comparing VF status between groups, those with neurophthalmological conditions had worse mean deviation values than those with glaucoma ( p = 0.01). Complete demographic and ocular data are shown on Table 1 .

Table 1

Parametersa

NO Group (n = 68)

NTG Group (n = 21)

P value

IOP (mmHg) b

17 (14.7, 18.7)

13 (12.7, 14.0)

p  < 0.01

Among the eyes with neurophthalmological diseases, 16 eyes (15.8%) were considered as conditions that could mimic glaucoma based on the fundus aspect. Looking at the most common diagnoses that filled the GON fundoscopic criteria, we found 4 (25%) cases of non-arteritic ischemic optic neuropathy, 3 (18.7%) cases of tumoral compressive optic neuropathy and 3 (18.7%) cases of hereditary optic neuropathy.

Regarding the neurophthalmological conditions that most often led to misdiagnosis as glaucoma, non-arteritic ischemic optic neuropathies and congenital optic disc anomalies were the most common diagnosis (two cases of each diagnosis). Finally, when considering the diagnostic ability of the glaucoma specialist, we found a diagnostic accuracy of 84.5%. More specifically, 11.9% of the glaucomatous neuropathies were misdiagnosed as neurophthalmological disorders, while 25% of the nonglaucomatous optic neuropathies were misdiagnosed as glaucoma.

Discussion

Discriminating glaucomatous from nonglaucomatous neuropathy can be a difficult task in clinical practice even for experienced professionals. Although glaucoma is the main cause of disc cupping, 20% of the patients can be misdiagnosed [ 10]. The results of this study support that nonglaucomatous optic neuropathies can mimic glaucoma specially when IOP is within the normal range, leading to misdiagnosis even when evaluated by a glaucoma specialist based on retinography and perimetry. Additionally, our study provides information about how often nonglaucomatous optic neuropathies may mimic NTG and the conditions that most frequently do so.

There are scant data about conditions that most often lead to misdiagnosis between NTG and nonglaucomatous optic neuropathies. The few previously published studies have pointed out isquemic, compressive and hereditary optic neuropathies as possible causes of nonglaucomatous cupping [ 7, 8, 10- 15]. These studies investigated specific parameters that could distinguish glaucomatous from nonglaucomatous cupping and their potential underlying mechanisms, but focusing solely on one specific optic neuropathy at a time. In this study, we had the opportunity to evaluate a large population of consecutive patients with miscellaneous nonglaucomatous optic neuropathies, highlighting those that most often mimic glaucoma and lead to misdiagnosis when evaluated by a glaucoma specialist. Using fundoscopic criteria, the most common diagnoses were isquemic, compressive and hereditary optic neuropathies, in this order of frequency. Looking at cases that were misdiagnosed as glaucoma, isquemic optic neuropathy and congenital anomalies were the most frequent ones. We believe these results may help in the differential diagnosis of glaucoma cases within normal pressure ranges.

We believe that the differentiation between glaucomatous and non-glaucomatous optic neuropathies is both clinically and economically relevant. In this context, clinicians often have to decide whether or not to request neuroimaging for patients with disc cupping and IOP within the normal range. The incorrect diagnosis of neurophthalmolgical conditions can not only lead to an unnecessary treatment with topical hypotensive drops of a nonprogressive hereditary optic neuropathy, for example, but also to more serious scenarios, such as a late diagnosis of a treatable intracranial tumor. Although this is not the aim of our study, some authors have suggested age <50 years, visual acuity <20/40, visual field defects respecting vertical midline, pallor of neuroretinal rim, asymmetrical loss of color vision and relative afferent pupillary defect as clinical parameters that should be evaluated when considering neurologic evaluation [ 8, 15], while others suggested considering neuroimaging screening in all patients with NTG [ 16]. We understand that all clinical parameters discussed so far must be individually considered when making the decision of whether or not to submit the patient to neuroimaging screening. Additionally, based on our findings, one should keep in mind the most frequent differential diagnosis and the most common conditions that lead to misdiagnosis while making this decision.

A secondary purpose of our study was to evaluate the ability of a glaucoma specialist to discriminate glaucomatous from nonglaucomatous neuropathy based on color fundus photographs and VF results. In our study, 88.1% of the glaucoma cases and 75% of the optic neuropathies cases were correctly classified. Other studies reported 75-80% accuracy in diagnosing glaucoma and a lower than 50% accuracy in diagnosing other optic neuropathies [ 10, 11]. However there are some methodological differences between our study and those previously mentioned that must be pointed out. First, in our study, the masked reader made the diagnosis based not only on the optic disc characteristics, but also on the perimetry data. Furthermore, the cases in our study only needed to be classified as glaucoma or not glaucoma, whereas the other previous studies included other differential diagnoses. We believe these significant differences preclude a straight comparison between the results of these studies and our findings.

It is important to emphasize some specific characteristics and limitations of our study. First, all patients were recruited in one single center and the evaluation of the mixed set of images was performed by one single examiner. Even though we had a large sample of more than 100 consecutive eyes with nonglaucomatous optic neuropathies and the examiner was an experienced glaucoma specialist, these potential sources of bias should be considered while interpreting our results. Second, the masked reader only had access to retinography and perimetry results, which does not correspond to the daily practice, where the ophthalmologist has access to other important clinical information (like age, visual acuity, pupillary reflexes and neurologic symptoms). Although this was not the main focus of our study, this fact should be taken into consideration, as it might have underestimated the diagnostic ability of the glaucoma expert. It is also important to highlight that the option of asking a glaucoma specialist to evaluate the mixed set of images was made because previous studies have already reported that NTG and neurophtalmological conditions are frequently misdiagnosed and this differential diagnosis represents a challenging situation in clinical daily practice. Considering a challenging diagnostic situation, a higher frequence of misdiagnosis would be expected if non-specialists were asked to evaluate those patients. Therefore, we believe that the specialists opinion represents the most accurate evaluation available in clinical practice and would be more appropriate than the general ophthalmologists evaluation in a challenging diagnostic situation.

Conclusion

In eyes with IOP in the normal range, some neurophthalmological disorders can mimic and be misdiagnosed as glaucoma. Among them, isquemic and compressive optic neuropathies were the most common conditions whose fundoscopic appearance resembled glaucomatous optic neuropathy. Almost one quarter of these eyes were misdiagnosed when evaluated by a glaucoma specialist - isquemic optic neuropathy and congenital anomalies being the causes that most often did so. These can lead to inadequate management and influence the prognosis of these patients.

Acknowledgements

We would like to thank Ms Alison Dowdell for editing and revising this manuscript.

Funding

There were no specific funding sources for this study nor for the preparation of this manuscript.

Availability of data and materials

The datasets analysed during the current study are available from the corresponding author on reasonable request.

Authors' contributions

DTD contributed to the study implementation, data acquisition and drafting of the manuscript. MU contributed to the study implementation, data acquisition and manuscript preparation. RB contributed to conception and design of the study and its implementation. SD contributed to the study implementation, data acquisition and critical revision of the manuscript. TSP conceived of the study and directed its implementation, data analysis and data interpretation. All authors read and approved the final manuscript.

Competing interest

The authors declare that they have no competing interest.

Consent for publication

Not applicable for this study.

Ethics approval and consent to participate

The institutional review board of the Federal University of São Paulo approved this study protocol. In addition, written informed consent was obtained from all participants.

Abbreviations

AION

Anterior ischemic optic neuropathies

GON

Glaucomatous optic neuropathy

HVF

Humphrey visual field

IOP

Intraocular pressure

NTG

Normal-tension glaucoma

POAG

Primary open angle glaucoma

VF

Visual field

Contributor Information

Diego Torres Dias, Email: rb.moc.kooltuo@serrot.ogeiD.

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References

7. Choudhari N, Fudnawala V, Neog A, George R. Cupped disc with normal intraocular pressure: The long road to avoid misdiagnosis. Indian J Ophthalmol. 2011;59(6):491. doi: 10.4103/0301-4738.86320. [[ PMC free article] [ PubMed] [ CrossRef] Google Scholar]

9. Kesler A, Haber I, Kurtz S. Neurologic evaluations in normal-tension glaucoma workups: are they worth the effort? Isr Med Assoc J. 2010;12(5):287-289. [[ PubMed] Google Scholar]

15. Fraser CL, White AJ, Plant GT, Martin KR. Optic nerve cupping and the neuro-ophthalmologist. J Neuroophthalmol. 2013;33(4):377-389. [[ PubMed] Google Scholar]

Articles from BMC Ophthalmology are provided here courtesy of BioMed Central

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Neurophthalmological conditions mimicking glaucomatous optic neuropathy: analysis of the most common causes of misdiagnosis

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To analyze the most common neurophthalmological conditions that may mimic glaucomatous optic neuropathy and to determine which most often lead to misdiagnosis when evaluated by a glaucoma specialist. We reviewed the charts of consecutive patients with optic neuropathies caused by neurophthalmological conditions screened in a single Eye Clinic within a period of 24 months.

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The analysis of fundnus photographs can be utilized to detect glaucoma, with reported accuracy of over 95% in making a positive diagnosis.

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Example 2 of existence of AI in Health Care "Haralick" diagnose of Glaucoma

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Example 2 of existence of AI in Health Care "Haralick" diagnose of Glaucoma

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Also useful in the diagnosis of Glaucoma. Differentiation of the texture of normal retinal image and glaucoma image is the key of diagnosis of glaucoma. "Haralick" features have been used to differentiate the image of normal and damaged retina. AI features( image extraction and by training of the back propagation neural network) have been utilize for the diagnosis of Glaucoma with 96% accuracy. (Bhattacharya S, Pradhan KB, Bashar MA, Tripathi S, Semwal J, Marzo RR, et. al)

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Example 1 of existence AI in Health Care. Help with Geriatric person

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Example 1 of existence AI in Health Care. Help with Geriatric person

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"To solve geriatric care related problem, Japan introduced AI-based robots. These help geriatric person, helping them in days to day activities, from taking the morning pills to adjusting the AC temperature while sleeping in the bed." (Bhattacharya S, Pradhan KB, Bashar MA, Tripathi S, Semwal J, Marzo RR, et. al)

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Siri, Alexa, and Google Assistant are only command-and-control systems. Future possibilities for Artificial Intelligence are broad.

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Siri, Alexa, and Google Assistant are only command-and-control systems. Future possibilities for Artificial Intelligence are broad.

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These can understand a finite list of questions . If the user asks the virtual assistant to do something that is not in its code, the bot simply says it can't help.
The author in the article stated: "In the future, the technologies of chatbots and voice assistants will coverage. That means people will be able to control chatbots with speech, and those who use Apple, Amazon, and Google products will be able to ask the virtual assistants to help them with their jobs, not just tasks like checking the weather."

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Life become easier with AI-based technologies

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Life become easier with AI-based technologies

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Example: Alexa, used for domestic use. It is capable of performing modern day household activities like voice recognition, voice interaction, etc. It can also control several devices like turning off TV or Ac during non-use.
(Bhattacharya S, Pradhan KB, Bashar MA, Tripathi S, Semwal J, Marzo RR, et. al)

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"This is a non-human intelligence which can do wonders with 90%-96% accuracy and reliability"

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"This is a non-human intelligence which can do wonders with 90%-96% accuracy and reliability"

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"Now we are living in the era of Artificial Intelligence (AI). In simpler words, AI means- inherited intelligence of a computer machine, which the computer learns by repeated use of end users (humans)."

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"The inequitable distribution of doctors in the Indian public sector poses a threat to the health and economic well-being of the people." (Bhattacharya S, Pradhan KB, Bashar MA, Tripathi S, Semwal J, Marzo RR, et. al)

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"In India, more than 833 million people are dependent on 44,000 doctors. Because of the maldistribution, on an average, each doctor serves 19,000 population. This situation is responsible for to ever-increasing out of pocket expenditures (OOP) on health. This in turn impoverishes 3.3% of India's population per year." (Bhattacharya S, Pradhan KB, Bashar MA, Tripathi S, Semwal J, Marzo RR, et. al)

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Bhattacharya S, Pradhan KB, Bashar MA, Tripathi S, Semwal J, Marzo RR, et al. Artificial intelligence enabled healthcare: A hype, hope or harm. J Family Med Prim Care 2019;8:3461-4.

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Abstract

In this paper, we have described the health care problem (maldistribution of doctors) in India. Later, we have introduced the concept of artificial intelligence and we have described this technology with various examples, how it is rapidly changing the health care scenario across the world. We have also described the various advantages of artificial intelligence technology. At the end of the paper, we have raised some serious concerns regarding complete replacement of human based health care technology with artificial intelligence technology. Lastly, we concluded that we have to use artificial intelligent technology to prevent human sufferings/health care problems with proper caution.

Keywords: Algorithm, artificial intelligence, doctors, healthcare, machine learning

The Existing Health Care Problem

Whether healthcare is to be provided in the government of private sector or in both, it is of utmost importance that availability of adequate number of doctors providing basic healthcare services in the under privileged areas is the key to improving health outcomes in our country. Some countries like India, the situation has changed. Now we have adequate doctors, the problem is maldistribution.[ 1]

A new National Health Policy and the National Health Assurance Mission are already in the place. It is high time to critically analyse the options available to policy makers for resolving the current situation.[ 1]

Nearly 69% of India's population resides in its villages, where only 26% (nearly) of its doctors serve, that too mostly in the private sector. This is very expensive and beyond the reach of common people. In India, more than 833 million people are dependent on 44,000 doctors.[ 2]

Because of the maldistribution, on an average, each doctor serves 19,000 population. This situation is responsible for to ever-increasing out of pocket expenditures (OOP) on health. This in turn impoverishes 3.3% of India's population per year.[ 3] This is about the quantity only. The issue of quality of care is also equally important. For positive health outcomes, there is a need of a healthy and viable health workforce which should be available to the populations 24 × 7.

Rao et al. (2012) have highlighted the impact of the maldistribution of the doctors in the Indian public sector as a double burden - a threat to the health of the population as well as its economic well-being.[ 4]

Artificial Intelligence-As Possible Solution

Among many options, we can think about introducing technology to tackle this crisis like other sectors, e.g. for reduction of bankers (manual) load use of IT enabled self-help kiosks. Both aviation and railway sectors have incorporated automation/technology in their operation to their advantage.[ 5]

Now we are living in the era of Artificial Intelligence (AI). In simpler words, AI means- inherited intelligence of a computer machine, which the computer learns by repeated use of end users (humans). This is a non-human intelligence which can do wonders with 90-96% accuracy and reliability.[ 5]

In the era of AI-based technologies, life has become easier, e.g., Alexa, is a virtual assistant developed by Amazon Lab126 used for domestic use. It is capable of performing modern day household activities like voice recognition, voice interaction, music playback, making to-do lists, providing real time information about the weather and traffic etc., Alexa can also control several smart devices like switching off TV or AC during non-use. Various extra skills (called apps) can be installed in Alexa which can increase its multiple capabilities.[ 6]

Artificial Intelligence in Health Care

In reference to the healthcare industry, to solve geriatric care related problems, Japan has already introduced AI-based robots (personally programmed). These help geriatric persons, helping them in day to day activities, from taking the morning pills to adjusting the AC temperature while sleeping in the bed.[ 7]

It is useful in the diagnosis of Glaucoma also. Glaucoma, an optic nerve disease, is considered as one of the major causes which can lead to blindness. Glaucoma is a Global Public Health Problem. Progressive rise in intraocular pressure leads to pressure atrophy of atrophy, resulting in unilateral or bilateral vision loss. Differentiation of the texture of normal retinal image and glaucoma image is the key to diagnosis of glaucoma. In this work, ' Haralick' features have been used to differentiate the image of normal and damaged retina. AI features (image extraction and by training of the back propagation neural network) have been utilized for the diagnosis of Glaucoma with 96% accuracy.[ 8]

Another example is UE Lifesciences and Niramai, who have adopted Artificial Intelligence to diagnose breast cancer accurately. In 2016, Niramai Health Analytics (Bangalore), developed a non-invasive, low-cost solution to screen early breast cancer based on mapping body heat embedded with artificial intelligence technique.[ 9] ' Niramai' is an acronym for Non-Invasive Risk Assessment with Machine-learning and Artificial Intelligence. This ' Thermalytix ' technology can detect tumours five years earlier than mammography or clinical exams. In this screening technique, no doctor or technician is required (i.e. no touch technique) while performing this screening test. The machine is portable and the costing is 1/10 (350-500 INR) of routine mammography (3500-5000 INR). Moreover, the skill set required for operating this machine is not very high. Nurses and paramedics can operate it very easily. This technology enables tele-radiology also for remote consultation, lowering full-time staffing costs for the hospitals. Now the company/technique is growing at a rapid pace. Till date, 12 hospitals have tied up with this company which stands for non-invasive risk assessment with machine learning. They have already screened nearly 4200 women and detected cancer early on in some of them.

In this test, the patient enters a room, and the instructions are given from outside. The test is carried out with complete privacy. Later, it was realised that the privacy component played a significant role, particularly for rural women. This resulted in easy and wider acceptance of this technology.[ 9, 10]

Nowadays, robot-assisted surgery is gaining popularity day by day. Surgical robots are capable of doing complex minimally invasive procedures with better visualization, increased precision, and enhanced dexterity compared to laparoscopy.[ 11] These are the examples of successful use of AI technology in the healthcare. It is generally agreed from the above discussion that all scientific work in the future will be changed by machine learning complex algorithms, artificial intelligence and robotics.

Artificial Intelligence and Human Intelligence

There are two domains of human capabilities - cognitive and physical. In the past, humans retained authoritative role over the machines due to cognitive advantage and machines conquered with humans mainly in raw physical capabilities. Hence, most of the manual jobs in agriculture sector and industry sector were automated. As a result, "new" type of jobs have emerged which was based, so far, on mainly of cognitive skills, e.g. learning, analyzing, communication and understanding human emotions.[ 12]

It is important to understand that the AI revolution is not just about high tech and super computers. It is now powered by breakthroughs in biological sciences as well.

The better we understand the biochemical mechanisms of human emotions, desires and choices, the better computers can become in analyzing and predicting human behaviours, and hence in replacing human drivers, bankers and lawyers.[ 13]

To gain a much better insight of decision making by humans, a few decades ago, scientists hacked data of neurosciences and behavioral economics. The research findings highlighted that our all choices/decisions/desires (from eating food, wearing dress) resulted not from some enigmatic intuitions, but were due to the probabilities calculated by billions of neurons within a fraction of second. In reality, overhyped human intuition is just pattern/event recognition.[ 14]

Good clinicians, surgeons, and nurses do not have magical powers about their patient's diagnosis. It is something like reconsidering recurring patterns/events. Usually, they identify the learned pattern/event in medical schools and try to avoid wrong things which may be wrong during the investigations, diagnosis, treatment or a wrong surgery. The study also revealed that the biochemical algorithms of the human brain are not absolutely perfect. According to the scientists, our neuronal biochemical algorithms are basically degenerated, short, outdated and adapted one.[ 14]

That is why good surgeons, clinicians sometime make foolish mistakes. From the above discussion, it is now clear that AI-enabled doctors can outperform human doctors in the cognitive domain as well.

As already discussed, diagnosing a disease by a doctor is not dependent upon magical powers/intuitions. Rather, their brains spot biochemical patterns by evaluating signs and symptoms of the patients. If AI is equipped with the right sensors and software, it could treat patients far more accurately and reliably than a human doctor. This infotech and biotech revolution holds promise to fill the gap of human doctors in the resource poor areas.[ 15]

What psychiatrists/neurologists are learning today about the basal ganglia, pons, cerebrum and the cerebellum might make it possible for computers to outperform human psychiatrists/neurologists in the future.

AI-enabled doctor may not only be able to hack humans and outperform them in their uniquely human skills, it may also have competitive advantage over unique non-human abilities. Thus, AI makes the difference between an AI enabled doctor and a human doctor who have mere owned a medical degree from medical schools. Among the non-human abilities that AI possesses, one practical advantage is of easy and quick connectivity and updateability.[ 15]

If we have to update the medical knowledge of all human doctors, it will be quite a time-consuming exercise, as all humans are individuals. It is not possible to connect them and update them in real time if any pandemic occurs.

In contrast, it is easy to integrate all AI-enabled doctors into a single flexible network. It means that we are not replacing millions of individual traditional doctors by millions of individual AI enabled doctors. Rather, an individual doctor is likely to be replaced by an integral network. Actually, we should compare the capabilities of a collection of human individuals to the abilities of an integrated network/AI-enabled doctors.[ 15]

Similarly, if WHO identifies a new infection, or if a laboratory produces a new drug, it is not possible to update all the traditional doctors about these developments. In contrast, the same information can be shared/updated among 10 billion AI-enabled doctors within a fraction of a second. They can all also communicate to each other their feedback on the new infections or drugs via non-neural networks.[ 15]

Some people might argue that by switching to AI-enabled doctors, we may lose the advantages of individual clinical judgement/creativity. As an example, if a traditional doctor makes a wrong diagnosis, he may kill that single patient. In contrast, if all AI-enabled doctors belong to a single system, and the system makes a mistake, the results might be devastating. In reality, an AI-enabled doctor can maximize the advantages the connectivity without the losing the benefits of individuality, the hallmark of AI.[ 16]

When we run many alternative algorithms on the same network, the patient in a remote village can access hundreds of AI-enabled doctors whose relative performance is constantly monitored and compared. The particular patient, if not satisfied with the AI-enabled doctor, can easily contact the "Traditional Doctor" for a second opinion.[ 16]

The future paybacks for human civilization are likely to be huge. AI enabled doctors could provide far better and cheaper healthcare for billions of under-served people. Thanks to machine learning and biometric sensors, a poor villager might get same healthcare via her/his smartphone as the richest person gets today from the modern urban health facility.[ 16]

Few Concerns about Artificial Intelligence

In healthcare, we, as a doctor, concentrate almost exclusively on processing the medical information. Usually we collect medical data, analyze it and make a provisional diagnosis/final diagnosis. In contrast to doctors, nurses require good psychomotor and emotional skills also to give a painful injection to the newborn or replace a bandage to the injured. That is why it is easy to have an AI-enabled family doctor on our smartphone than a reliable nurse robot.

The healthcare industry which takes care of the sick, injured, disabled is likely to remain a human support for a long time. Therefore it is difficult now to comment upon the complete replacement of emotional component of humans. However, missing of human touch may pose a problem, because at the end of the day, the most valuable and non-replaceable thing is human-human interaction.[ 16]

The Conclusion

However, we cannot expect the rate of technology adoption in public health sector to be same as other sectors. Banking, tourism, and transport sectors usually adopt newer technologies at a higher speed than public health sector. This is because in every technological transaction, profit is involved. It is not the same for public health sector. Which is why we have to remain patient for some period to enjoy the fullest benefit of this technology.

Lastly, we cannot ignore the technological advancement. We have to use technology to prevent human sufferings with proper caution. Now it will be interesting to watch in the future that how much (in diagnosis/treatment/decision making) AI-enabled doctors can contribute to manage the "maldistribution" of doctors in India and address health problems of the community. It is high time to experiment with AI enabled doctors in other resource poor countries.
N.B. The opinion expressed in this article is that solely of the author and does not reflect the official position of any other affiliated institution or agency affiliated in past or present.

Acknowledgements

We do acknowledge all previous researchers who has been worked and still working on this topic (AI). We do also acknowledge Prof. Yuval Noah Harari, for inspiring us to write this article.

References

3. Furtado KM, Kar A. Health resources in a 200,000 urban Indian population argues the need for a policy on private sector health services. Indian J Community Med. 2014;39:98-102. [[ PMC free article] [ PubMed] Google Scholar]

4. Rao M, Rao KD, Kumar AKS, Chatterjee M, Sundararaman T. Human resources for health in India. Lancet. 2011;377:587-98. [[ PubMed] Google Scholar]

8. Samanta S, Ahmed SS, Salem MA-MM, Nath SS, Dey N, Chowdhury SS. Haralick features based automated glaucoma classification using back propagation neural network. In: Satapathy SC, Biswal BN, Udgata SK, Mandal JK, editors. Proceedings of the 3rd International Conference on Frontiers of Intelligent Computing: Theory and Applications (FICTA) 2014. Springer International Publishing; 2015. pp. 351-8. (Advances in Intelligent Systems and Computing) [ Google Scholar]

13. Kahneman D. Thinking, Fast and Slow. 1st ed. New York: Farrar, Straus and Giroux; 2013. p. 499. [ Google Scholar]

15. Jiang F, Jiang Y, Zhi H, Dong Y, Li H, Ma S, et al. Artificial intelligence in healthcare: Past, present and future. Stroke and Vascular Neurology. 2017;2:230. [[ PMC free article] [ PubMed] Google Scholar]

Articles from Journal of Family Medicine and Primary Care are provided here courtesy of Wolters Kluwer -- Medknow Publications

# data[].attributes.attachment_link.title
Artificial intelligence enabled healthcare: A hype, hope or harm

# data[].attributes.attachment_link.description
In this paper, we have described the health care problem (maldistribution of doctors) in India. Later, we have introduced the concept of artificial intelligence and we have described this technology with various examples, how it is rapidly changing the ...

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Can AI-enabled healthcare solution surpass human healthcare in terms of accuracy, efficiency, and effectiveness? 

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Can AI-enabled healthcare solution surpass human healthcare in terms of accuracy, efficiency, and effectiveness? 

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What are the ethical implication of of replacing human healthcare with AI-enabled doctors solution and how does the concerns regarding the loss of human touch and emotional support in patient care? 

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What are the ethical implication of of replacing human healthcare with AI-enabled doctors solution and how does the concerns regarding the loss of human touch and emotional support in patient care? 

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How can the healthcare balance the adoption of AI technology while maintaining the essential human interaction for patient care? 

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How can the healthcare balance the adoption of AI technology while maintaining the essential human interaction for patient care? 

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What are potential advantages and limitations of AI-enabled doctors compared to human doctors in healthcare?

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What are potential advantages and limitations of AI-enabled doctors compared to human doctors in healthcare?

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How can artificial intelligence be effectively utilized to address the issue of doctors in India provide affordable and high-quality healthcare to common populations and improve the healthcare access in general?

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How can artificial intelligence be effectively utilized to address the issue of doctors in India provide affordable and high-quality healthcare to common populations and improve the healthcare access in general?

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Artificial intelligence vs. Human intelligence

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Artificial intelligence vs. Human intelligence

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Artificial intelligence as a possible solution

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Artificial intelligence as a possible solution

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The existing healthcare problem in India

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The existing healthcare problem in India