The Legal and Ethical Challenges of AI in Healthcare: Liability, Privacy, and Regulatory Frameworks
For the last century, healthcare has progressed at an unprecedented rate, making strides in every aspect of its field and keeping society healthy and efficient. In the last decade, artificial intelligence (AI) has proven to be the new catalyst of the field, contributing to new advancements that can revolutionize how healthcare is run globally. Specifically, the application of technology and AI in healthcare can address some of these challenges. [1] Through exploring this intersection of technology and healthcare, the field as a whole can be efficiently and safely transformed. However, it is also important to consider the legal and ethical challenges when implementing AI, such as data privacy, algorithm bias, and accountability. With AI being heavily reliant on large sets of data, harnessing vast amounts of patient data can be compliant with privacy regulations and HIPAA laws. [2] Therefore, AI's intersection with the medical field must take place in a cautious, guided manner that ensures the protection of human rights while allowing the health industry to flourish.
The benefits of AI in healthcare are often overlooked due to how fast the technology is growing and making strides in the field. According to the National Institute of Health, AI can increase the efficiency of the healthcare system by analyzing massive amounts of data quickly and accurately, lowering the workloads of healthcare professionals, and thereby enhancing patient care. [3] Furthermore, AI has allowed for enhanced diagnostic accuracy, personalized treatments, resource optimization, and significant cost reductions by streamlining workflows and analyzing vast amounts of patient data. [4] However, it is also critical to note the underlying risks of AI in medicine. Specifically, one key issue includes the “black box” problem, where complex algorithms can only be viewed in terms of their inputs and outputs, with no way to understand the AI's algorithm. This is specifically problematic because patients, physicians, and even designers do not understand why or how a treatment recommendation is produced by AI technologies. [5] This issue creates a lack of trust within these models, which can be harmful when they control such large sets of patient information. Due to this widespread lack of trust, fully integrating AI in healthcare can prove to be both dangerous and potentially harmful.
Another key factor that needs to be considered when using AI in healthcare is the liability complications that arise. Specifically, it needs to be clear who is liable when AI makes a mistake, and how to differentiate between traditional medical malpractice and AI-driven decisions. Many individuals take the stance that the creators of autonomous AI should assume liability for harms when the device is used properly and on-label, and obtain medical malpractice insurance. [6] Responsibility for the proper use and maintenance of devices remains with the providers. In case of assistive AI, the physician remains fully liable. I also agree with this, as medical professionals aren't purposefully trying to induce harm or danger when an AI tool makes a mistake. A related example to such liability concerns includes the $4 billion AI Failure of IBM Watson for Oncology Case, where a project that was meant to revolutionize cancer treatment ultimately fell far short of its goals and collapsed. The main issue was that Watson's knowledge base was heavily influenced by Memorial Sloan Kettering Cancer Center practices, leading to recommendations that often failed to align with local guidelines or real-world cases. [7] This lack of diversity in training data undermined the system's global applicability. Due to this, the AI training model was unreliable and created liability concerns within healthcare as a whole.
The privacy and regulatory frameworks of AI's integration in medicine have been the largest barrier to its success. Currently, AI in healthcare operates within a framework of existing and emerging regulations, including HIPAA, FDA regulations for Software as a Medical Device (SaMD), and the General Data Protection Regulation (GDPR), emphasizing data privacy, security, and ethical use to safeguard patient information and ensure responsible AI implementation. [8] However, the critical issue at hand is that existing models of regulation are designed for locked healthcare solutions, whereas AI is highly versatile and evolves over time. Therefore, it is crucial that lawmakers and regulators are quick and consistently keep up with developing legislation and regulations. Only then can they ensure responsible innovation while simultaneously protecting our basic human rights. [9]
With AI rapidly advancing and making strides in nearly every field, it is important that society maintains a balance between innovation and ethical responsibility. While the progress made for AI in healthcare is proving to be rewarding and beneficial, its legal and ethical challenges, which form confusion regarding liability and privacy, are important to consistently consider as the technology becomes increasingly prevalent. To address these concerns proactively, institutions should implement clear regulatory standards that define accountability for AI-driven decisions, require transparency in algorithmic processes, and enforce robust patient data protection measures. This includes creating legal frameworks that assign liability when AI systems cause harm, mandating explainability in clinical decision-making tools, and establishing independent oversight bodies to audit algorithms for bias or misuse. Only by embedding these 1safeguards into the infrastructure of AI development and deployment can we ensure that technological advancement supports, rather than compromises, patient trust and well-being.
Edited by Ava Betanco-Born
Endnotes
[1] National Center for Biotechnology Information, “Artificial Intelligence in Healthcare: Past, Present and Future,” NCBI, 2021, https://pmc.ncbi.nlm.nih.gov/articles/PMC8285156/.
[2] DJ Holt Law, “The Legal Risks of AI in Healthcare: Minimizing Liability and Ensuring Compliance,” DJ Holt Law, 2024, https://djholtlaw.com/the-legal-risks-of-ai-in-healthcare-minimizing-liability-and-ensuring-compliance/#:~:text=Privacy%20Violations%3A%20AI%20systems%20often,significant%20legal%20and%20reputational%20consequences.
[3] National Center for Biotechnology Information, “Applications of Artificial Intelligence in Health Care: Review and Future Directions,” NCBI, 2024, https://pmc.ncbi.nlm.nih.gov/articles/PMC10887513/#:~:text=AI%20can%20also%20increase%20the,enhancing%20patient%20care%20%5B48%5D.
[4] Park University, “AI in Healthcare: Enhancing Patient Care and Diagnosis,” Park University, 2024, https://www.park.edu/blog/ai-in-healthcare-enhancing-patient-care-and-diagnosis/#:~:text=Enhanced%20Diagnostic%20Accuracy%20%E2%80%93%20AI%20can,providing%20better%20care%20to%20patients.
[5] ScienceDirect, “Adoption of AI in Health Care: Challenges and Opportunities,” ScienceDirect, 2023, https://www.sciencedirect.com/science/article/pii/S2667102623000578.
[6] Henrico Dolfing, “Case Study: IBM Watson for Oncology Failure,” Henrico Dolfing, December 2024, https://www.henricodolfing.com/2024/12/case-study-ibm-watson-for-oncology-failure.html#:~:text=Watson's%20knowledge%20base%20was%20heavily,undermined%20the%20system's%20global%20applicability.
[7] BHM Healthcare Solutions, “Regulatory and Ethical Considerations in AI Adoption in Healthcare Settings,” BHMPC, September 2024, https://bhmpc.com/2024/09/regulatory-and-ethical-considerations-in-ai-adoption-in-healthcare-settings/#:~:text=Healthcare%20AI%20operates%20within%20a%20framework%20of,data%20protection%20and%20privacy%20in%20the%20EU.
[8] Ernst & Young, “How the Challenge of Regulating AI in Healthcare Is Escalating,” EY, 2024, https://www.ey.com/en_gl/insights/law/how-the-challenge-of-regulating-ai-in-healthcare-is-escalating.