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Healthcare right now is experiencing a rapid transformation of AI. This padlet will look over the impact of technology with the help of AI in healthcare. We will also see the positive and negatives outcomes.

On a positive note, AI is advancing the diagnostics like taking a deeper dive into the medical images, such as X-rays, and MRI's. With the incredible speed of AI, they often catch diseases quicker and with more accuracy. This also makes high personalized medicine just by analyzing the patients genetic history, which will give a treatment process to follow. Beyond all the medical work, AI overpowers, basic task that a person can do, such as scheduling for doctors and patients, billing and improving the hospital efficiency. For example modern robots that will do the cleaning over a stay of someone.

Never less, even with all the positives of AI, there comes negatives. Without training AI there are many concern about a person data privacy and security, while many of these people will be in hard times this makes them more common to all these threats. Lastly, AI will have a very high cost, so to maintain or even implement them could leave smaller hospital in debt.

My project will talk about the positives and negatives of AI in the healthcare. From the robots to giving a machine a scan and giving you back a treatment plan.

A little bit of personal insights my brother works in a hospital, and every day he will call me and tell me about the new changes. They only have a cleaning robot but he says he wouldn't be surprised if they will have AI all over the work place.

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What I have learned about AI in healthcare is that, it can be very beneficial. There are so many positives that everyone is so scared of. There was not a lot of challenges that I faced, I was able to find articles and videos about every topic and they were very good. I think that healthcare should really think about including AI in hospitals, I think the best way to start is small, little by little add AI into hospitals and give the people what they want and that is a report if this is beneficial.

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An ambulance ride can cost a lot of money, but what if you didn't need to take one and save yourself all that money. A study in the UK shows that 350,000 people a month are taken to a hospital by ambulance. All of these is up to the paramedics and they say who should be taken in the ambulance. Now 80% of cases from AI show that they could correctly be taken the hospital by an ambulance. The AI was trained on the paintets blood oxygen levels, pulse and chest pains in this case to determine if they were in need of a ride. The benefits of this, are a quicker response time from location to hostpial. Some challenges are, if AI calculates something wrong and they take themselves and something happens who is it to blame. I have a personal connection with this because I had to take an ambulance ride for something very minor and it was a 2 minute ride. So now I think if my mom could have driven me how much money could we have saved.

https://www.weforum.org/stories/2025/08/ai-transforming-global-health/

https://www.facebook.com/lseps/videos/can-ai-save-the-nhs-lse-research/1373297330390960/

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The last case I want to look into is from harvard, they talk about AI can do task that are not as important. This is so they can make more time to be face to face with their patients. With many of the healthcare doctors feeling overwhelmed with their work load if AI can do some of the easy task it will reduce stress. “Doctors can also use AI algorithms to write letters to patients, to collect past history and summarize it to support medical decision making, and to select the best medicine for each patient’s specific case." Benefits of this are doctors are less likely to crash out. Some challenges are this may lead to doctors not feeling like they know more about their patients. I found this intersting because I have seen a lot of doctors being overworked because they have too much to do.

https://learn.hms.harvard.edu/insights/all-insights/benefits-latest-ai-technologies-patients-and-clinicians

https://www.youtube.com/watch?v=hunZH8PZPeM

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The Benefits of the Latest AI Technologies for Patients and Clinicians | Harvard Medical School Professional, Corporate, and Continuing Education

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Today, artificial intelligence (AI) is changing the trajectory of health care—from automating routine tasks and increasing efficiency to improving diagnoses, accelerating the discovery of more effective treatments, and so much more.

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Future Trends and Ethical Considerations

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Future Trends and Ethical Considerations

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AI is transforming various ways, including healthcare and the way they transform clinical practice, and there outcomes. Such as drug development, disease diagnoses, treatment and patient monitoring. Looking ahead in the next years I think we can accept a growing trend of collaboration within healthcare. Their are some legal frameworks with AI in healthcare. Countries like Japan, South Korea, and China have all developed AI- specific laws, with specific laws in healthcare. While countries having laws, the FDA does support AI, they have issued medical devices and has issued guidance on AI drug development. Some recommendations would be the approval process and many required safeguards based on the risk of AI. There is also the professional accountability side, where they would have to determine who would be at fault if AI is wrong. Something they could do is have no-fault compensation funds, which there would be compensation without needing fault to be proven. Some concerns about this is the deployers and developers need to address when they are using AI in healthcare. Improper treatment risk is the major one, if there is a risk when training data fails to account of patient populations, and this can lead to risky outcomes. After reading this article I am very unsure about AI in healthcare. I think that if healthcare can try it for a certain amount of time. With no bad outcomes I would feel more confident.

https://www.aoshearman.com/en/insights/ao-shearman-on-life-sciences/ai-in-healthcare-legal-and-ethical-considerations-at-the-new-frontier

https://www.youtube.com/watch?v=XSfv86B7KBY

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AI is already transforming drug discovery and diagnosis-and has the potential to do likewise across other elements of the healthcare industry. But to get there will require robust regulatory frameworks to address safety, privacy, and bias concerns-as well as scenario-based assessments to ensure AI's safe and ethical deployment.

Can human beings cure all diseases in our lifetime? For centuries, humanity has strived to cure diseases. With the advent of Artificial Intelligence (AI), the dream of a disease-free world seems more attainable than ever before. AI holds immense potential to revolutionize healthcare, promising enhanced care quality, expedited drug development, and reduced costs. Some experts predict that "AI will be as common in healthcare as the stethoscope." However, the rapid advancement of AI also potentially heightens certain risks, such as safety concerns, privacy issues, and bias. It is therefore essential to establish robust regulatory and ethical frameworks to manage them effectively.

AI's roles in healthcare

AI is revolutionizing healthcare by enhancing various aspects such as drug development, disease diagnosis, treatment, patient monitoring, and administrative tasks. Notable examples include Google's Med-PaLM, Stanford's CheXNet, and NVIDIA's partnership with Hippocratic AI. In addition to the advancements by the private sector, the World Health Organization (WHO) launched S.A.R.A.H. (Smart AI Resource Assistant for Health) in April 2024. This digital health promoter prototype, powered by generative AI, features enhanced empathetic responses in eight languages.

Looking ahead, we can expect a growing trend of collaboration among healthcare companies, technology firms, and research institutions. This synergy will drive further innovations and improvements in healthcare delivery and patient outcomes.

Legal frameworks governing AI in healthcare

Regulating AI in healthcare is an intricate task that involves striking a balance between fostering scientific innovation and protecting human rights and safety. Different countries may adopt various approaches to AI regulation, reflecting their unique values and priorities. For instance, jurisdictions such as the European Union (EU), Japan, South Korea, and China have AI-specific laws, while others, including the UK, U.S., and Australia, are applying existing technology-neutral laws to AI. These diverging regulatory approaches result in significant compliance burdens for companies deploying and building AI.

We believe that effective regulation of AI in health requires international collaboration. By working together, countries can create a cohesive framework that enhances human welfare on a global scale. This collaborative effort can help ensure that AI technologies are used safely and ethically, while also promoting innovation and protecting human rights.

Overview of AI legal frameworks

Current AI legal frameworks

International organizations and governments are actively engaging with stakeholders to develop regulations and industry standards. Currently, most of these guidelines are principle-based, focusing on the fair and equitable use of AI. For instance,

The WHO has published various guidelines on AI in healthcare, emphasizing ethical considerations and best practices. These guidelines stress the importance of designing and using AI systems in ways that respect patient privacy, promote equity, and mitigate biases.

In 2024, the Organization for Economic Cooperation and Development (OECD) updated its AI Principles, marking the first intergovernmental standard on AI. These principles aim to balance innovation, human rights, and democratic values.

From the perspective of legislation by sovereign states, the legal landscape for AI in healthcare is still in its infancy and continues to evolve. Many countries are currently relying on existing technology-neutral laws, such as data protection and equality laws, as well as industry standards, to address AI-related matters. Additionally, some nations are taking proactive steps to develop approaches to address issues arising from AI technologies.

In the U.S., the Food and Drug Administration (FDA) has recently issued several discussion papers on AI drug development and manufacturing medical devices and guidance on decentralized clinical trials. FDA generally supports the use of AI in healthcare development and has already reviewed and authorized over 1200 AI/Machine Learning (ML)-enabled medical devices. In addition, the Center for Drug Evaluation and Research (CDER) of the FDA has established the Framework for Regulatory Advanced Manufacturing Evaluation (FRAME) Initiative to support the adoption of advanced manufacturing technologies that could bring benefits to patients.

In the EU, the AI Act is recognized as the world's first comprehensive AI law. Although most of its requirements will only come into effect from August 1, 2026, and pure research and development AI is excluded from much of its scope, the Act imposes regulatory requirements on AI systems based on four risk categories: (1) prohibited AI, (2) high risk AI, (3) AI triggering transparency requirements, and (4) general-purpose AI. In the context of healthcare, the middle two categories-"high risk AI" and "AI triggering transparency requirements"-are likely to be the most relevant. These categories will impose specific regulatory obligations to ensure the safe and ethical use of AI in healthcare applications.

We are also increasingly seeing healthcare companies using general purpose AI models (GPAIM) for many hundreds of different use cases, across R&D and corporate functions. This is typically by way of customizing large language models using proprietary data. As such, the industry has been calling out for clarification regarding the extent to which such bespoke deployment of GPAIM will engage the specific EU AI Act obligations (applying from August 2, 2025).

Whilst the EU Commission's guidelines ⁵, published in July 2025, offer some insight as to the compute threshold at which downstream modification constitutes the creation of a new model (with that downstream modifier then becoming a "provider" of the GPAIM and therefore subject to extensive compliance requirements), simple numerical thresholds do not necessarily tell the whole story. There are many different techniques for customizing general purpose AI models, and a simple compute threshold will not capture some customization techniques that are likely to have a more significant impact on model behavior, such as system prompts. Careful case-by-case consideration of the modification in practice will be necessary.

Organizations at risk of falling within scope of the EU AI Act GPAI requirements should consider the relevance of the General Purpose AI Code of Practice (the GPAI Code) ⁶. The GPAI Code, while non-binding, has been developed collaboratively under the leadership of the European AI Office and is intended to be a practical tool to support organizations in complying with the AI Act for GPAI models, addressing transparency, copyright and safety and security in particular. The drafting process sparked significant debate among stakeholders: some arguing that the GPAI Code is overly restrictive with calls for greater flexibility, particularly regarding the training of LLMs. However, the European Commission asserts that signatories will benefit from a "simple and transparent way to demonstrate compliance with the AI Act", with enforcement expected to be focused on monitoring their adherence to the GPAI Code. It remains to be seen how organizations manage that adherence, particularly, for example, in the face of technical challenges (such as output filtering) and legal complexities (not least due to the interplay with ongoing court action) and the allocation of liability between provider and deployer.

Unlike the EU, the UK has, to date, chosen not to pass any AI-specific laws. Instead, it encourages regulators to first determine how existing technology-neutral legislation, such as the Medical Device Regulations, the UK GDPR, the Data Protection Act, can be applied to AI uses. For example, the Medicines & Healthcare products Regulatory Agency (MHRA) is actively working to extend existing software regulations to encompass "AI as a Medical Device" (or AIaMD). The MHRA's new program focuses on ensuring both explainability and interpretability of AI systems as well as managing the retraining of AI models to maintain their effectiveness and safety over time.

In China, the National Health Commission and the National Medical Products Administration recently published several guidelines on the registration of AI-driven medical devices and the permissible use cases of applying AI in diagnosis, treatment, public health, medical education, and administration. The guidelines all emphasize AI's assisting roles in drug and medical device development and monitoring under human supervision.

Leading AI developers are also setting up in-house AI ethics policies and processes, including independent ethics board and review committee, to ensure safe and ethical AI research. These frameworks are crucial while the international landscape of legally binding regulations continues to mature.

Recommendations: scenario-based assessments for AI tools

Healthcare companies face a delicate balancing act. On one hand, their license to operate depends on maintaining the trust of patients, which requires prioritizing safety above all else. Ensuring that patients feel secure is non-negotiable in a sector where lives are at stake. On the other hand, being overly risk-averse can stifle the very innovations that have the potential to transform lives and deliver better outcomes for patients and society as a whole. Striking this balance is critical: rigorous testing and review processes must coexist with a commitment to fostering innovation, ensuring progress without compromising safety.

In this regard, a risk-based framework is recommended for regulating AI in healthcare. This approach involves varying the approval processes based on the risk level of each application. Essentially, the higher the risks associated with the AI tools, the more controls and safeguards should be required by authorities. For instance, AI tools that conduct medical training, promote disease awareness, and perform medical automation should generally be considered low risk. Conversely, AI tools that perform autonomous surgery and critical monitoring may be regarded as higher risk and require greater transparency and scrutiny. By tailoring the regulatory requirements to the specific risks, we can foster innovation while ensuring that safety is adequately protected.

Moreover, teams reviewing AI systems should consist of stakeholders representing a broad range of expertise and disciplines to ensure comprehensive oversight. For example, this may include professionals with backgrounds in healthcare, medical technology, legal and compliance, cybersecurity, ethics and other relevant fields as well as patient interest groups. By bringing together diverse perspectives, the complexities and ethical considerations of AI in healthcare can be better addressed, fostering trust and accountability.

Data protection and privacy

Data privacy requirements are a key consideration when using AI in healthcare contexts, especially given that many jurisdictions' laws broadly define "personal data," potentially capturing a wide range of data. Further, privacy regulators have been the forerunners in bringing AI-related enforcement actions. For example, AI tools such as OpenAI's ChatGPT have encountered extensive regulatory scrutiny at EU level through the European Data Protection Board (EDPB) taskforce and NOYB (None of your Business)/the European Center for Digital Rights, the data privacy campaign group founded by Max Schrems, the well-known privacy activist, has initiated a complaint against the company in Austria, alleging GDPR breach. DeepSeek has also attracted immediate attention from EU and other international regulators, with investigations initiated and the EDPB taskforce extended to cover its offerings.

Privacy considerations in AI

There are several privacy considerations to navigate when using AI. This can raise challenges as, often U.S. based, developers look to navigate highly regulated jurisdictions such as those in the EU, where regulators are scrutinizing approaches taken to data protection compliance. This includes the issue of identifying a lawful basis for the processing activity. Many jurisdictions' data privacy laws contain a legitimate interests basis or similar provisions which, when applicable, permit the data controller to process personal data without first requiring individuals' explicit consent. However, there are diverging views on whether this basis can be used for AI-related processing.

The European Data Protection Board (EDPB) issued an Opinion 28/2024 ⁷ in December 2024, which provides detailed guidance on the use of legitimate interest as a legal basis for processing personal data in the development and deployment of AI models, including LLMs (the EDPB AI Opinion). The EDPB AI Opinion, although indicating that legitimate interest may be a possible legal basis, highlights the three-step test that should be applied when assessing the use of legitimate interest as a legal basis, i.e. (1) identify the legitimate interest pursued by the controller or a third party; (2) analyse the necessity of the processing for the purposes of the legitimate interest pursued (the "necessity test"); and (3) assess that the legitimate interest is not overridden by the interests or fundamental rights and freedoms of the data subjects (the "balancing test"). It also highlights the need for robust safeguards to protect data subjects' rights. The examples where legitimate interests could be a suitable lawful basis in the EDPB AI Opinion are relatively limited, including examples such as a conversational agent, fraud detection and threat analysis in an information system.

An EDPB Opinion adopted a few months earlier, in October 2024, which addresses the legitimate interests basis for processing of personal data more generally (the EDPB LI Opinion), while helpful in referencing scientific research as a potential legitimate interest, is cautious about establishing a legitimate interest on the basis of societal benefit, emphasizing that the legitimate interest should tie to the interest of the controller or third party and that processing should be "strictly" necessary to achieve the legitimate interest (i.e. there is no other reasonable and equally effective method which is less privacy intrusive).

The EDPB AI Opinion clarifies that the unlawful processing of personal data during the development phase may not automatically render subsequent processing in the deployment phase unlawful, but controllers must be able to demonstrate compliance and accountability throughout the lifecycle of the AI system.

Individual consent

As an alternative, businesses may need to obtain individual consent for AI-related processing activities. While this can be a difficult basis to use given the high bar for valid consent, it can be particularly challenging in an AI healthcare context given the heightened compliance obligations that apply to special category data (which includes health data), raising the requirement for consent to "explicit consent" combined with the potential for public distrust and misunderstanding around AI technologies.

Further, in some jurisdictions it is common for individuals to place stringent conditions, including time restrictions, on what their personal data can be used for. This could prevent their personal data being used in connection with AI, given it is not always possible to delete or amend personal data once it has been ingested into an AI system.

Professional accountability

Determining fault when an AI system makes an error is a particularly complex issue, especially given the number of parties that may be involved throughout the value chain. The challenge is heightened by the fact that different regulations may apply at different stages, and the legal landscape is still developing in response to these new technologies.

In the case of fully autonomous AI decision-making, one possible approach is that liability could fall on the AI developer, as it may be difficult to hold a human user responsible for outcomes they do not control. However, the allocation of responsibility could vary depending on the specific circumstances and regulatory frameworks in place.

Where AI systems operate with human involvement, another potential approach is for regulators to introduce a strict liability standard for consequences arising from the use of AI tools. While this could offer greater protection for patients, it may also have implications for the pace of technological innovation. Alternatively, some have suggested that requiring AI developers and commercial users to carry insurance against product liability claims could help address these risks. The WHO, for example, has recommended the establishment of no-fault, no-liability compensation funds as a way to ensure that patients are compensated for harm without the need to prove fault. ⁸

In July 2025, a study commissioned by the European Parliament's Policy Department for Justice, Civil Liberties and Institutional Affairs, was published ⁹. Its aim was to critically analyze the EU's evolving approach to regulating civil liability for AI systems, four policy proposals are discussed and the report advocated for a strict liability regime targeting high-risk AI systems.

Ultimately, the question of legal responsibility for AI in healthcare remains unsettled and is likely to require ongoing adaptation as technology and regulation evolve. Accountability will be a particular challenge given the complexity of the value chain and the interplay of different regulatory regimes. It will be important for all stakeholders to engage in continued dialogue to ensure that legal frameworks keep pace with technological developments and that patient safety remains a central focus.

Ethical concerns

There are multiple ethical considerations that developers and deployers may need to address when using AI systems in healthcare. Three prominent examples are explored below.

Bias causing unjust discrimination

Bias in AI systems can lead to unjustified discriminatory treatment of certain protected groups. There are two primary types of bias that may arise in healthcare:

Disparate impact risk: This occurs when people are treated differently when they should be treated the same. For example, a study¹⁰ found that Black patients in the U.S. healthcare system were assigned significantly lower "risk scores" than White patients with similar medical conditions. This discrepancy arose because the algorithm used each patient's annual cost of care as a proxy for determining the complexity of their medical condition(s). However, less money is spent on Black patients due to various factors including systemic racism, lower rates of insurance, and poorer access to care.¹¹ Consequently, using care costs created unjustified discrepancies for Black patients.

Improper treatment risk: Bias in AI systems can arise when training data fails to account for the diversity of patient populations, leading to suboptimal or harmful outcomes. For example, one study¹² demonstrated that facial recognition algorithms often exhibit higher error rates when identifying individuals with darker skin tones. While this study focused on facial recognition, the same principle applies in healthcare, where AI systems used for dermatological diagnoses have been found to perform less accurately on patients with darker skin.¹³ This occurs because the datasets used to train these systems often contain a disproportionate number of images from lighter-skinned individuals. Such biases can lead to misdiagnoses or delays in treatment, illustrating the critical need for diverse and representative training data in healthcare AI applications.

Transparency and explainability

Providing individuals with information about how healthcare decisions are made, the process used to reach that decision, and the factors considered is crucial for maintaining trust between medical professionals and their patients. Understanding the reasoning behind certain decisions is not only important for ensuring high-quality healthcare and patient safety, but also helps facilitate patients' medical and bodily autonomy over their treatment. However, explainability can be particularly challenging for AI systems, especially generative AI, as their "black box" nature means deployers may not always be able to identify exactly how an AI system produced its output. It is hoped that technological advances, including recent work on neural network interpretability, ¹⁴ will assist with practical solutions to this challenge.

Human review

To facilitate fair, high-quality outcomes, it is important for end-users-often healthcare professionals-to understand the AI system's intended role in their clinical workflow and whether the AI system is intended to replace user decision-making or augment it.

However, it may not always be appropriate for the human to override the AI system's output; their involvement in the workflow will likely vary depending on what the AI tool is being used for. For example, if an AI system has been trained to detect potentially cancerous cells in skin cell samples, and the AI system flags the sample as being potentially cancerous but the healthcare professional disagrees, it may be more appropriate to escalate the test to a second-level review than to permit the healthcare professional to simply override the AI system's decision. A false positive here is likely to be less risky than a false negative. It is therefore important to take a considered, nuanced approach when determining how any human-in-the-loop process flow should operate.

Conclusion

AI offers significant benefits in healthcare but also presents legal and ethical challenges that must be navigated. Collaborative efforts among policymakers, healthcare professionals, AI developers, and legal experts are essential to establish robust frameworks that safeguard patient rights and promote equitable access to advanced healthcare technologies.

This article was written by Jieni Ji and David Egan, assistant general counsel, global digital and privacy at GSK in London.

1. K. Savchuk. " AI Will Be as Common in Healthcare as the Stethoscope. " May 15, 2024.

2. In the U.S., there is no comprehensive federal legislation that regulates the development of AI to date. The White House recently released the U.S. AI Action Plan, which directs various U.S. agencies to take steps to invest in and enable vastly greater AI infrastructure in the U.S., foster AI innovation, and export U.S. AI innovation internationally while protecting US trade secrets. The federal government is also seeking to quell AI regulation at U.S. State level, however there are many hundreds of AI-focused regulations that have been enacted or proposed across U.S. states, resulting in a fragmented landscape.

3. The U.S. FDA. Download "Conducting Clinical Trials Decentralized Elements Guidance for Industry, Investigators, and Other Interested Parties. " September 2024.

4. The U.S. FDA. "Artificial Intelligence and Machine Learning (AI/ML)-Enabled Medical Devices." July 10, 2025.

5. General Purpose AI Guidelines, July 18, 2025.

6. General Purpose AI Code of Practice, July 10, 2025

7. EDPB Opinion 28/2024 on certain data protection aspects related to the processing of personal data in the context of AI models.

8. WHO. "Ethics and governance of artificial intelligence for health: Guidance on large multi-modal models." 2024.

9. European Parliament. " State of Play of Academic Freedom in the EU Member States." Study, Directorate-General for Internal Policies, Policy Department for Citizens' Rights and Constitutional Affairs, 2025.

10. Obermeyer, Z., Powers, B., Vogeli, C., & Mullainathan, S. " Dissecting racial bias in an algorithm used to manage the health of populations. " Science, 366(6464), 447-453 (2019).

11. Hoffman, K.M., Trawalter, S., Axt, J.R., & Oliver, M.N. " Racial bias in pain assessment and treatment recommendations, and false beliefs about biological differences between blacks and whites. " Proceedings of the National Academy of Sciences, 113(16), 4296-4301 (201 6).

12. Buolamwini,J., & Gebru, T., " Gender shades: Intersectional accuracy disparities in commercial gender classification ", Proceedings of Machine Learning Research, 81, 1-15 (2018).

13. Melanoma Research Alliance. " Making AI Work for People of Color: Diagnosing Melanoma and Other Skin Cancers". Melanoma Research Alliance. 2022.

14. Shaham T., Schwettmann S., Wang F., et al. " A Multimodal Automated Interpretability Agent." Forty-first International Conference on Machine Learning. 2024.

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AI in healthcare: legal and ethical considerations at the new frontier

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AI holds immense potential to revolutionize healthcare, promising enhanced care quality, expedited drug development, and reduced costs, however its rapid advancement also potentially heightens certain risks.

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How is AI in healthcare having an impact on society, that is the question most people ask. The truth is that the technology and innovation is all ready having a significant social impact. AI provides essential support, and tools that is, streamlining the flow of work and the transform of work. AI is intelligent enough to provide a trusted support to the healthcare workers that are battling hard workloads, and an increase in their patients. The hard reality is doctors in healthcare are super busy, demanding, and stressful lifestyles. They are always under constant pressure trying to perform well, AI systems are developing the amount of errors that are made because of how much of exhaustion they are in. AI is a pair of eyes that will never shut, and a brain that won't ever turn off, AI is offering an extra safety net that is supporting the workload and lives more efficiently. I think this is great, AI can offer another set of everything in the hospitals. This really makes me think a different way with AI, I always thought AI was awful.

https://www.aidoc.com/learn/blog/social-impact-artificial-intelligence/#the-social-impact-of-artificial-intelligence-in-healthcare-

https://www.youtube.com/watch?v=VG5Z_Qu_r_E

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The Social Impact of Artificial Intelligence (AI) on Healthcare- Healthcare AI | Aidoc Always-on AI

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Artificial intelligence (AI) is set to make a significant social impact on the healthcare industry thanks to its ability to provide ongoing support to overwhelmed medical practitioners and practices. In a recent analysis of how AI is having an impact on society and bioethics, the researchers point to how AI and the technology that surrounds […]

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https://www.aidoc.com/learn/blog/social-impact-artificial-intelligence/#the-social-impact-of-artificial-intelligence-in-healthcare-

I chose this article because they gave good points about the social impact, and gave very good positives about AI.

https://www.oracle.com/health/ai-healthcare/

I enjoyed this article because there multiple cases about AI being used in the healthcare.

https://www.weforum.org/stories/2025/08/ai-transforming-global-health/

This was a great article because most of them that I read talked about the doctors, but none talked about they ways that they get to the hospital.

https://learn.hms.harvard.edu/insights/all-insights/benefits-latest-ai-technologies-patients-and-clinicians

This was cool to read because it gave me a chance to look at the benefits of how AI can free about the doctors time by doing all the little things.

https://www.aoshearman.com/en/insights/ao-shearman-on-life-sciences/ai-in-healthcare-legal-and-ethical-considerations-at-the-new-frontier

This was one of the last articles I read, and I though it was different because it didn't just talk about the US healthcare but it also talked about different countries healthcare.

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Healthcare- Cases of AI being used in healthcare

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Healthcare- Cases of AI being used in healthcare

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The first case study I want to address is medical imaging. AI - analysis of X-rays, MRI's and CT scans, can help doctors with more accurate diagnose. AI tools can be used to help spot areas of scans with the highest probability of tissue growth. The benefits of this would be AI could spot something a human eye can not catch, but the disadvantages of this would be AI is not trained like the human eye so they may catch something that is not even there. Challenges are you would have to train AI to catch what you are looking for. I think this is a very cool topic, because instead of waiting for results and then doctors looking over a result, AI can do it so much quicker and give so many more results.

https://www.oracle.com/health/ai-healthcare/

https://www.youtube.com/watch?v=3DUyzPvsMQ8