# data[].attributes.body
Obviously, as seen throughout the other examples, there's a lot of good with the bad.

One of the most obvious trends in the future of healthcare is drug discovery and development. As generative AI models can use software that can predict how different molecules interact, it could possibly push the research and development of new drugs faster than we have ever seen. Fairly recently, pharmaceutical company AstraZeneca had signed a 555 million dollar deal with a biotech company Algen, using an AI model called AlgenBrain. It will use CRISPR gene modifications as well as AI in order to target immune diseases. According to Adams (2025), "The deal allows AstraZeneca exclusive rights to develop and sell therapies 'against a defined set of targets identified and selected through the partnership,' the company said in a statement." Not only is this exciting, but I believe that it can lead to specialized medicines that might work in certain demographics of people better than others. This allows for patients to be treated as strong as doctors and health care professionals possibly can, and I think it's exciting to think that AI can revolutionize medicine in that way.

Obviously, there's a drawback which involves how AI can handle the data and the information associated with patient privacy. HIPAA and patient privacy is stressed so much already in the world of healthcare that there's a lot of grey area when AI starts to handle massive amounts of information. This is seen with Cense AI, which in 2020, massive folders that had patient information was leaked onto the internet, including names of patients and their information particularly with neck or spinal injuries. Alder (2020) explains that, "The data was quite detailed and included patient names, addresses, dates of birth, policy numbers, claim numbers, diagnosis notes, payment records, date of accident, and other information. The majority of individuals in the data set appeared to come from New York. In total, there were 2,594,261 records exposed across the two folders."

Clearly, this begs an even bigger problem, and that's who to blame in the future. When the AI starts to handle itself, does it fall on the hospitals that use it, the developer, or is it up in thin air? That's an issue that I believe won't be solved until new laws get put into place regarding AI, which I feel are definitely needed to prevent wrongdoing.

# data[].attributes.attachment_link.title
Transforming paperwork into patient care: Omega Healthcare’s automation journey

# data[].attributes.headline
The Societal Issues Regarding AI in the Healthcare System

# data[].attributes.subject
The Societal Issues Regarding AI in the Healthcare System

# data[].attributes.body
As promising as AI is for the industry, there's one big issue worth mentioning in relation to society.

The lack of jobs.

AI will fundamentally get rid of the people that process insurance claims, get rid of staff that process patients like receptionists, remove the need for large amounts of radiologists to look at scans, as well as possibly the researchers that look into some of the different aspects of the health industry. As powerful as AI is, it's also a very large problem for the lower level workers in the healthcare industry, and I fear that when AI actually gets advanced enough, these jobs will disappear.

One other large issue to add on top of this is the bias that already is seen in the models today. This is because AI is primarily trained using white subjects, which can cause lots of different problems for other ethnicities and other groups that need health care. Not having as much data for someone who is a person of color vs. a Caucasian person is a clear issue in the whole scheme of healthcare, which is to try to treat everyone to the best of their ability. This isn't able to happen effectively if there are biases with AI in general. Even though I really do believe that AI can transform society and especially the world of healthcare, I think that, overall, if widescale use is employed, it will be a disaster for those seeking entry-level jobs in the realm of healthcare.

# data[].attributes.body
One example of AI being used in the healthcare setting is the use of AI in the realm of patient care, specifically with Mayo Clinic and cardiovascular health.

Mayo Clinic, which is one of the leading resources for health information as well as being one of the best hospitals in the US, is known for its innovative techniques in the world of medicine. They sought to use AI in the world of cardiovascular health, using it to train models in order to detect heart disease earlier, particularly by using the trained models to identify strokes and other trauma events in imaging. The video embedded shows the use of AI in a condition called amyloidosis, which is when deposits of amyloid proteins clump and attach to the heart.

According to the Mayo Foundation for Medical Education and Research (2025), "Applying AI to ECGs has resulted in a low-cost test that can be widely used to detect the presence of a weak heart pump. A weak heart pump can lead to heart failure if left untreated." Mayo Clinic, which used machine learning AI to detect what a weak heart pump looks like, came from a database of nearly 7 million ECGs, allowing for much faster diagnoses and image processing time. However, one challenge that had to be dealt with was the patient information that usually were left on the scans. In order to uphold HIPAA's standards, all information that could identify a patient was removed before the images could be used to train the AI.

I thought that this was not only an interesting use of AI, but it also just simply makes sense. AI can be trained to detect patterns with things like a specific scan or imaging in the hospital setting and can do it at a speed much faster than a human probably could. This leads to quicker diagnosis times that could potentially save the life of a patient.

# data[].attributes.attachment_link.title
Detecting cardiac amyloidosis using AI and the ECG

# data[].attributes.body
Another example of AI being used in the healthcare system is its use in data management. The company Omega Healthcare, which is one of the largest revenue cycle management companies in the US, handles vast amount of data relating to medical codes, patient billing, and insurance claims.

Usually, all of this work is done with manual entry, meaning humans have to look into each claim and verify it, meaning there's possibility for errors, high costs, and delays. To combat this, they partnered with AI company UiPath, which uses machine learning and natural language processing in order to identify information, and then uses a system called robotic process automation, which can mimic the actions of humans on a computer, such as clicking a mouse.

First, the data must be ingested, meaning that the AI has to process what the data is, say if its an insurance claim or whatnot. Then, the AI extracts the data and structures it so that it is verified to be accurate and then is filled out using RPA and later sent to healthcare databases or billing systems. Above is a diagram on how the information is processed.

According to Sweeney (2024), "...the company found that automation was saving employees more than 15,000 hours a month...Omega Healthcare has also reduced the amount of time workers spend on documentation tasks by 40%. It has also slashed document processing turnaround time by 50% with a process accuracy of 99.5%." Not only does this show how the AI is being used, but it also how it is improving the lives of the employees of the company who no longer have to constantly sift through all the data and worry about errors.

I feel that this is a double-edged sword. Being that the AI performs a lot better than humans can at the task, there's a large chance that the AI would replace the workers who actually do these tasks themselves. Not only would it save the company money, but it would also likely just be more proficient. One other interesting thing I believe is worth mentioning is that, in the event of a data breach, it is possible that this system could leak thousands, if not hundreds of thousands of patient data with no real way of stopping it.

# data[].attributes.body
The research process has not only taught me about the vast amounts of companies that are using AI in the system of healthcare, but also how they all are very different. Obviously, there are many components to healthcare and patient treatment, but it's very interesting to be able to look at the different companies and how they all use AI systems with different types of AI to complete a task. Another thing that I have gained from the research is also the drawbacks that come with employing AI in a system that is completely dominated by humans, even in the low level positions. Job security and patient data protection are two very large issues that will need to be addressed in the future if AI has a place in the industry. I truly believe AI can be a great thing in the world of patient care, but if it isn't properly managed, it could mean the loss of jobs of many. Overall, the research has helped not only broaden my knowledge on how it is used in everyday life, but also has opened my eyes to the potential issues with using these systems.

# data[].attributes.body
The healthcare system is the realm of people, resources, and facilities designed to provide proper care and health services to meet needs of others. It's a system that has been around for ages, but recently, the scene has been making room for artificial intelligence. AI in the world of healthcare is not only rapidly expanding, but it is also reaching out to make great breakthroughs and other achievements. AI is being used in many aspects of the world of health, such as data management and recognizing different patterns without much human input. This can be used in areas such as patient care, research, and management.

As someone who is hopefully moving towards a career in the healthcare system right a time where AI is making numerous breakthroughs, it is interesting to see what possibilities these machines can provide in not only the world of patient care, but also in regards to research being done. It's fascinating to look at how much progress has been made especially when taking into consideration how AI has helped pioneer the COVID-19 Vaccine as well as many other achievements in recent times. However, it is also easy to look at how potentially damaging the use of AI in the industry could be.

The Padlet will provide examples of AI being used in the healthcare system involving patient care, data management, and research. It will also give insight onto the future of AI in the space of health as well as its impact. The end will contain my reflection on what I have gained from my research as well as my personal experiences from it.

# data[].attributes.body
In 2023, researchers at MIT's Schwarzman College of Computing used a machine learning algorithm to create an antibiotic that can kill a specific type of bacteria known as Acinetobacter baumannii.

According to Trafton (2023), “'Acinetobacter can survive on hospital doorknobs and equipment for long periods of time, and it can take up antibiotic resistance genes from its environment. It’s really common now to find A. baumannii isolates that are resistant to nearly every antibiotic,' says Jonathan Stokes, a former MIT postdoc who is now an assistant professor of biochemistry and biomedical sciences at McMaster University."

Using machine learning, they used a library of 7,000 potential antibiotics, they first placed A. baumannii in a petri dish with them, showing the results of grown to the model. Then the researchers used it to analyze nearly 7,000 the model hadn't seen before. The results from the test were given within 2 hours. They were given 240 that could potentially work, but the one that ended up being effective. The drug was named abaucin.

Obviously, the research was sped up by a massive amount than having to test each one individually. The idea that AI can speed up research is very clear, and I believe that it will obviously help produce drugs and advance other medical breakthroughs.

# data[].attributes.attachment_link.title
Using AI, scientists find a drug that could combat drug-resistant infections | Abaucin

# data[].attributes.body
Adams, B. (2025, October 6). AstraZeneca, Algen Biotechnologies Pen $555m AI Pact for Immunology targets. AstraZeneca, Algen Biotechnologies Pen $555M AI Pact for Immunology Targets. https://www.fiercebiotech.com/biotech/astrazeneca-algen-biotechnolgies-pen-555m-ai-pact-immunology-targets [This article provided insight on the deal between AstraZeneca and Algen Technologies for the use of AI for immunological disease research.]

Alder, S. (2020, August 21). AI Company Exposed 2.5 million Patient Records Over the Internet. The HIPPA Journal. https://www.hipaajournal.com/ai-company-exposed-2-5-million-patient-records-over-the-internet/ [The article explained Cense AI and their mishap with the breach in patient data, which was important for understanding the issues with AI in the system of healthcare.]

Mayo Foundation for Medical Education and Research. (2025, May 10). Artificial Intelligence (AI) in Cardiovascular Medicine. Mayo Clinic. https://www.mayoclinic.org/departments-centers/ai-cardiology/overview/ovc-20486648 [The article gives an example for how AI is being used in the patient care setting of health care and how it is benefitting others currently.]

Sweeney, E. (2024, June 4). Omega Healthcare is using AI to save employees 15,000 hours a month. Business Insider. https://www.businessinsider.com/omega-healthcare-uipath-ai-document-processing-health-transactions-2025-6 [The article discusses Omega Healthcare's use of an AI system in order to better sort data and assist their employees for more efficient and accurate results.]

Trafton, A. (2023, May 26). Using AI, scientists find a drug that could combat drug-resistant infections. MIT Schwarzman College of Computing. https://computing.mit.edu/news/using-ai-scientists-find-a-drug-that-could-combat-drug-resistant-infections/ [The article explains the research process done at MIT using AI to provide an antibiotic that is used for a specific type of bacteria that was previously not known before, highlighting AI's place in research.]