The Unseen Hand: Navigating Liability Issues with AI Medical Errors
The integration of Artificial Intelligence (AI) into healthcare, from diagnostic tools to robotic surgery, promises a revolution in patient care. Yet, as AI systems become more autonomous, a critical legal and ethical question emerges: Who is liable when AI makes a medical error? This is a pressing challenge for digital health professionals and legal experts, as current legal frameworks, primarily designed for human error, struggle to accommodate algorithmic failures, creating a "liability gap" that threatens both patient safety and innovation.
The Traditional Framework: Medical Malpractice
In the United States and many common law jurisdictions, medical errors are typically addressed under the doctrine of medical malpractice, a form of negligence. To establish liability, a plaintiff must prove four elements: duty, breach, causation, and damages, based on the standard of a "reasonable physician under similar circumstances."
When an AI system is involved, the initial liability often falls to the physician in the loop. If a doctor relies on an AI-driven diagnosis but fails to exercise their own professional judgment, they may still be held liable. The AI is often viewed as a sophisticated tool, and the physician remains responsible for its appropriate use and interpretation—a concept known as the "learned intermediary" doctrine.
Shifting the Blame: Product Liability and Manufacturers
However, the physician-centric model breaks down when the error is one of design or manufacture. If an AI system is flawed—due to poor training data, a coding error, or a failure to warn users about its limitations—the liability may shift to the AI developer or manufacturer under product liability law. This typically involves claims of manufacturing defect, design defect, or failure to warn.
A major challenge is the black box problem. The proprietary nature and complexity of deep learning models make it difficult for a plaintiff to prove a design defect. Moreover, AI systems are constantly learning and evolving, blurring the line between a static "product" and a dynamic "service," which complicates the application of traditional product liability law.
The Future of Liability: Shared Responsibility and Regulatory Gaps
As AI systems become truly autonomous, making decisions without direct human oversight, the legal landscape must evolve. Some scholars propose a model of shared liability, where responsibility is apportioned among the physician, the hospital (under vicarious liability), and the manufacturer.
Globally, regulators are beginning to respond. The European Union’s proposed AI Act and revised product liability directives aim to create a more explicit framework for AI-related harm. In the US, the FDA’s approach to regulating AI as a medical device is a step toward defining standards, but it does not fully resolve the tort liability question.
The core issue remains: current tort law is based on fault, but AI errors can occur without a clear human fault. Is it fair to hold a physician liable for an error they could not have reasonably foreseen, or a manufacturer liable for an error that emerged from a statistically sound but ultimately flawed training dataset?
For more in-depth analysis on this topic, the resources at www.rasitdinc.com provide expert commentary on the intersection of digital health, regulatory policy, and the future of medical liability.
Conclusion
The path forward requires a multi-pronged approach: clearer regulatory standards for AI development, greater transparency in algorithmic decision-making, and potentially new legal doctrines that recognize the unique nature of autonomous AI systems. Addressing this liability gap is crucial to ensure that the promise of AI in medicine is realized responsibly, protecting both patients and the innovators driving digital health forward.
References
[1] Cestonaro, C. (2023). Defining medical liability when artificial intelligence is involved. BMC Medical Ethics, 24(1), 1-11. [2] Mello, M. M., et al. (2024). Understanding Liability Risk from Using Health Care AI. New England Journal of Medicine: Health Law, Ethics, and Policy. [3] Grossbard, E. S. (2025). Legal Liability When an Autonomous AI Robot is Your Doctor. University of Miami Business Law Review, 33(1). [4] Maliha, G. (2021). Artificial Intelligence and Liability in Medicine: Balancing Safety and Innovation. The Milbank Quarterly, 99(4), 952-983. [5] Sullivan, H. R. (2019). Are Current Tort Liability Doctrines Adequate for Addressing Injury Caused by AI? AMA Journal of Ethics, 21(2), 147-154. [6] Di Mauro, L., et al. (2025). The role of artificial intelligence in analyzing clinical data in malpractice disputes. International Journal of Medical Informatics. [7] Terranova, C., et al. (2024). AI and professional liability assessment in healthcare. A forensic perspective. Frontiers in Medicine, 11. [8] Neri, E., et al. (2020). Artificial intelligence: Who is responsible for the diagnosis? La radiologia medica, 125(12), 1233-1238. [9] Health AI Partnership. (2025). Who Is—and Who Should Be—Liable for Medical AI Errors? Insight Series. [10] Stanford HAI. (2024). Who's at Fault when AI Fails in Health Care? News & Analysis.