Accountability crisis? Healthcare AI adoption surges while liability frameworks lag behind

A new report warns that artificial intelligence in healthcare could create complex legal challenges when determining liability for medical errors or poor patient outcomes. The findings highlight growing concerns about accountability as AI tools rapidly expand across clinical settings without adequate testing or regulatory oversight.

What you should know: Legal experts identify multiple barriers that could complicate malpractice cases involving AI systems.
• Patients may struggle to prove fault in AI design or implementation due to limited access to information about how these systems work internally.
• Multiple parties involved in AI development and deployment could point fingers at each other when problems arise, creating contractual disputes over liability.
• Courts can handle these issues, but early inconsistencies and uncertainties will likely increase costs across the AI healthcare ecosystem.

The big picture: AI development in healthcare has boomed without corresponding advances in evaluation methods or regulatory frameworks.
• Researchers are creating numerous AI tools, from scan interpretation algorithms to diagnostic assistance systems and hospital management platforms for bed capacity and supply chains.
• Many AI healthcare tools operate outside the oversight of regulators like the U.S. Food and Drug Administration.
• “One of the things that came up during the summit was [that] the tools that are best evaluated have been least adopted. The tools that are most adopted have been least evaluated,” said Prof. Derek Angus of the University of Pittsburgh.

Why this matters: The disconnect between AI adoption and proper evaluation could put patients at risk while creating legal uncertainty for healthcare providers.
• Regulatory approval doesn’t guarantee improved health outcomes, and AI tools can be deployed unpredictably across different clinical settings with varying user skill levels.
• Current assessment approaches are expensive and cumbersome, while many AI tools need to be in clinical use to be fully evaluated.

What they’re saying: Experts emphasize the need for better funding and infrastructure to properly assess AI performance in healthcare settings.
• “There’s definitely going to be instances where there’s the perception that something went wrong and people will look around to blame someone,” Angus warned.
• Prof. Glenn Cohen from Harvard Law School noted that “the interplay between the parties may also present challenges for bringing a lawsuit – they may point to one another as the party at fault.”
• Prof. Michelle Mello from Stanford Law School acknowledged that while courts can resolve these issues, “it takes time and will involve inconsistencies in the early days.”

Key details: The findings come from a comprehensive report by the Journal of the American Medical Association’s AI summit, which brought together clinicians, technology companies, regulatory bodies, insurers, ethicists, lawyers, and economists to examine AI’s role in healthcare and its associated challenges.