Clinical AI liability: an emerging risk in healthcare

As healthcare organizations accelerate their adoption of AI, technology is beginning to play a bigger role in clinical care itself, beyond access and administration. AI-enabled tools help organizations make faster diagnoses, streamline triage, and operate more efficiently.

As these tools become more embedded in care delivery, they raise an important question: Who is responsible when AI-assisted care contributes to patient harm?

This is where clinical AI liability comes in. It refers to the legal responsibility — whether that falls with the clinician, the healthcare organization, or the technology vendor — when something goes wrong, and it represents a growing area of risk across the industry.

Understanding this landscape can help organizations deploy tools responsibly, support better outcomes, and manage legal exposure.

How clinical AI liability shows up in care

Clinical AI liability isn’t just a theoretical concern. It can show up in familiar care scenarios where people, processes, and technology intersect. When an AI tool contributes to harm, failures often fall into three overlapping categories that together shape how liability may be evaluated:

1. Algorithmic error happens when an AI model produces inaccurate or biased results because of flawed design, unrepresentative training data, or performance drift over time. A risk-stratification tool, for example, may systematically underestimate risk in certain populations, leading to delayed escalation or missed diagnoses.
2. User error involves incorrect inputs, misinterpretation of outputs, or using a tool outside its intended purpose. That might mean entering incomplete medication lists or relying on a recommendation that conflicts with clinical signs.
3. System failure stems from implementation breakdowns, such as weak electronic health record (EHR) integration or workflow gaps that prevent information from reaching the right clinician at the right moment. For example, a critical AI-generated flag can be buried among notifications or fail to make it into the handoff when a patient moves between care settings.

Liability incidents often involve a combination of all three. A triage tool may be trained on the wrong population (algorithmic error), deployed without clear guidance or oversight (system failure), and used by a clinician who didn’t document why they followed its recommendation over their own judgment (user error). When a patient is harmed, plaintiffs and regulators will look across the full chain to understand what went wrong.

Even with new technologies, most AI-related patient care claims are still evaluated through traditional medical malpractice and negligence principles. Courts and claimants may ask familiar questions:

• Did the clinician and organization meet standards of care? 
• Did they act as reasonably prudent professionals would, given what they know — or should have known — about the AI tool’s capabilities and limitations?
• Was there a clear rationale for accepting or rejecting its recommendation, and is that reasoning documented?

Regardless of whether a tool is used for simple decision support or a more autonomous function, human oversight remains central to defensible practice. Courts may look closely at whether clinicians could question or override AI outputs, whether organizations built clear escalation pathways, and whether those safeguards were used when something didn’t look right.

Practical steps to reduce legal exposure

Many of the most effective mitigation strategies are extensions of sound clinical governance and risk management practices. Consider these high-impact steps:

1. Evaluate tools before deployment. 
   Assess performance in your actual patient population, test for subgroup bias, and conduct human‑factors and workflow testing before rolling out AI tools broadly. Pilot in controlled settings to confirm thresholds and alerting logic align with clinical priorities and avoid alert fatigue.
2. Define clear governance and intended use.
   Establish policies that spell out where and how a tool should be used, required training, escalation triggers, and override procedures. Assign ownership for model stewardship, including safety oversight and version management.
3. Train clinicians on trust, skepticism, and documentation.
   Provide competency‑based training using realistic cases that show when to rely on outputs, when to question them, and how to reconcile conflicts between AI recommendations and clinical judgment. Require documentation of the tool’s role in care, including the rationale for accepting or rejecting a suggestion.
4. Monitor performance and drift over time.
   Use dashboards, periodic calibration checks, and reviews of false positives and false negatives to detect degradation or emerging biases. Maintain a change‑control process for model updates and be prepared to suspend or retire tools when concerns arise.
5. Align contracts with your liability posture.
   Vendor agreements should address accuracy claims, update cadence, support obligations, data rights, and security responsibilities. Indemnification and warranty provisions should align with your malpractice and cyber liability coverage so that AI‑related claims don’t fall into gaps between policies.

These measures may help strengthen defenses when a claim arises. They can also support more consistent care delivery and help clinicians and organizations use AI more responsibly.

Is your insurance coverage built for this?

Many insurance programs were designed for a time when patient care, technology, and cyber exposures were treated separately, which can create uncertainty when an AI‑related incident spans them all. Organizations should review whether their current program addresses AI‑enabled patient injury and how different policies may respond.

Marsh Agency’s latest report takes a deeper look at how healthcare organizations can evaluate their current coverage, identify gaps, and design programs that better match the way they deliver care today. As you expand your use of AI‑enabled tools, now is the right time to ask whether your risk management practices — and your insurance program — are keeping pace with the changing landscape of clinical AI liability.