Navigating AI Liability in Healthcare: Key Considerations for Health System Leaders

Why AI Liability in Healthcare Matters

The growing integration of AI into healthcare workflows is reshaping medical liability. While AI promises substantial improvements in clinical decision-making and patient outcomes, it also brings inherent risks. AI systems rely on datasets from various sources, which may contain inaccuracies or biases. These flaws can lead to patient harm and raise difficult questions about who bears legal responsibility.

Liability concerns are already influencing AI adoption decisions. According to a recent American Medical Association survey, 82% of clinicians identified being shielded from liability for AI-driven errors as a key factor in encouraging AI adoption. Despite its importance, the legal framework around AI accountability remains unclear. This space is evolving rapidly, with more open questions than definitive answers, and health systems must take a proactive approach to address these emerging liability risks effectively.

Key Actors in AI-related Liability

AI-related liability in healthcare involves multiple stakeholders, each potentially bearing some responsibility:

• Clinicians: Physicians, nurses, and other providers using AI in patient care.

• Healthcare Institutions: Health systems and hospitals that procure, implement, and oversee AI tools.

• AI Developers/Vendors: Organizations that build, train, and maintain AI systems.

• Malpractice Insurers: Firms that provide liability coverage and adjudicate claims involving AI-enabled care.

Physician Responsibility and the "Standard of Care"

Most jurisdictions apply primary tort liability and traditional negligence principles when evaluating clinician liability. Malpractice claims typically assess whether the clinician acted reasonably under the circumstances. If not, and the deviation caused harm, liability may follow.

AI complicates this traditional framework. On the one hand, AI is currently seen as merely a decision-support tool, rendering clinicians accountable for clinical decisions. On the other hand, many diagnostic AI tools operate without clearly showing how they reach their decisions, making it difficult to understand or trace the results. As a result, there is a considerable risk that evidence-based standards of care are misinterpreted. This complexity makes it difficult to assess and assign liability. Moreover, because clinicians often lack influence over procurement decisions and may not have the technical capacity to evaluate AI safety and effectiveness, relying solely on clinician liability is problematic in a rapidly consolidating healthcare environment.

Developer Liability

Strict product liability typically applies to tangible products, not services. Historically, the law has viewed software, including clinical decision-support tools, as a service rather than a product. This categorization limits the scope for product liability claims against developers. However, this may evolve. Courts may reconsider this distinction as AI systems become more autonomous and less interpretable.

FDA regulatory status also matters. AI systems receiving full premarket approval (PMA) generally enjoy broader preemption protections from state-law claims. However, only a few AI-based medical devices have so far gone through PMA. Instead, most AI tools on the market today have been cleared via the FDA’s 510(k) pathway. These tools are treated as "substantially equivalent" to existing devices and are not broadly shielded from state-law tort liability.

Hospital and Health System Liability

Hospitals and health systems may face liability in two key ways:

• Vicarious (Derivative) Liability: Health systems can be held responsible for the actions of their employees when those actions involve improper AI usage.

• Direct Liability: Institutions may be liable for negligent procurement, implementation, oversight, or staff training related to AI systems.

Given that institutions, not individual clinicians, typically choose which AI systems to deploy and have the resources to govern and monitor their performance, legal exposure may increasingly shift toward organizations. AI tools are also highly sensitive to local data patterns, meaning that a model trained on one population may perform differently when deployed elsewhere. This makes post-deployment monitoring critical and highlights the growing responsibility of health systems to track AI efficacy and safety over time, underscoring even more the shifting liability towards health systems over any other party involved.

Shared Responsibility

When AI is added to the equation, malpractice liability must address the new responsibilities of clinicians, healthcare organizations, AI developers, and insurers. It’s not simply black and white. For instance, some provider organizations have in-house developers. In that case, the healthcare organization is both the developer and the provider. We are likely to see legal liability align with a shared responsibility approach.

In general, clinicians must use their medical judgment and training rather than depend solely on AI interpretation. Healthcare organizations must diligently integrate AI into their operations, monitor performance, and manage risks. AI developers must ensure their tools are accurate, unbiased, and clinically validated. Insurers must consider adapting coverage and claims processes to account for the evolving risks and liabilities introduced by AI in clinical settings.

Practical Takeaways for Health System Leaders

• Thorough Vetting: Carefully assess AI tools’ training data, validation studies, and regulatory status. Ensure the tools meet established industry standards and are supported by reliable evidence before implementation. Regularly incorporate AI-related risks into your organization's routine risk assessments to identify and manage potential vulnerabilities.

• Clear Policies & Governance: Define when and how clinicians should utilize AI in patient care. Establish and regularly update clear guidelines and documentation requirements to maintain accountability, consistency, and transparency. Implement governance structures such as oversight committees to clarify roles and oversight responsibilities.

• Performance Monitoring: Regularly monitor AI systems to ensure they are updated consistently to maintain accuracy and promptly address any identified issues. This ongoing monitoring and regular training reinforce due diligence in responsible AI use.

• Workforce Training: Provide continuous education and training for clinicians to inform them about the latest AI functionalities, limitations, and best practices.

• Insurance & Contracts: Regularly review and update indemnity clauses, insurance coverage, and vendor agreements to reflect AI-related risks and responsibilities. Collaboration with insurers can ensure alignment between liability coverage and evolving AI applications in healthcare.

• Future-Proofing: Anticipate legal and regulatory changes as AI increasingly integrates into clinical workflows. Remain proactive by frequently reviewing policies, compliance measures, and governance frameworks to keep pace with evolving standards.

A Promising Path Forward

We must tackle liability issues as AI becomes more integrated into clinical decisions. Establishing clear boundaries regarding accountability will promote greater transparency and responsible use of AI in healthcare. Collaboration among all stakeholders is essential to protect patients and enable providers to deliver care effectively while fostering innovation for a better future.