AI Misdiagnoses: AI in Healthcare and the Liability Chain From Software Developer to Hospital

Artificial intelligence is now woven into the daily work of American medicine, reading scans, flagging risk, suggesting diagnoses, and drafting notes. When it works, it works fast and often well. When it fails, the question is rarely whether someone made a mistake. The question is which link in a long chain of decisions and design choices broke first. From the developer who wrote the algorithm to the physician at the bedside, every link carries different legal exposure, and Michigan patients harmed by AI-influenced care often have multiple potential defendants.

At the Developer’s Door

Software developers have historically been insulated from medical malpractice claims because malpractice law is built around the doctor-patient relationship. That insulation is eroding.

Plaintiffs increasingly bring product liability claims against developers under theories of design defect and failure to warn, the same framework that governs every other product that injures a user. If an algorithm was trained on biased or insufficient data, if it produces unreasonably dangerous outputs in foreseeable circumstances, or if its limitations were not adequately disclosed to the medical professionals using it, the developer may be on the hook.

Two practical hurdles slow these cases. The first is the FDA’s clearance process. Many medical AI tools reach the market through the 510(k) or De Novo pathway, and developers often argue that clearance preempts state-law claims.

The second hurdle is causation. AI systems are notoriously opaque, and proving that a specific algorithmic output caused a specific injury, rather than the physician’s interpretation of it, has stopped some cases at the threshold. Other cases have produced jury awards against AI software developers for patient injury, showing the door is not closed.

The Hospital in the Middle

Hospitals occupy the most operationally complex link in the chain. They choose which AI tools to deploy. They train, or fail to train, clinicians on how to use them. They monitor, or fail to monitor, outputs over time. When something goes wrong, hospitals can face direct negligence claims for systemic failures that have nothing to do with any individual physician’s judgment.

Courts considering hospital liability for AI-related injuries are looking at duties such as:

• Vetting an AI tool before deployment, including evaluating the data it was trained on.
• Validating the tool against the hospital’s actual patient population.
• Training clinicians to interpret AI outputs critically, including known limitations.
• Monitoring real-world performance over time and pulling tools that drift or fail.
• Updating algorithms when manufacturers issue revisions or warnings.
• Obtaining informed consent when AI plays a meaningful role in diagnosis or treatment.

A Michigan hospital that adopts a powerful tool, hands it to undertrained clinicians, and never audits the results is not a passive intermediary. It is an active source of risk.

The Physician at the Bedside

The clinician using the tool still bears primary responsibility for the patient in front of them. AI is a sophisticated decision-support tool, not a substitute for clinical judgment. A Michigan physician who follows an AI recommendation without independent review and misses a diagnosis that the algorithm got wrong has not transferred the duty of care to the software. The duty stays with the doctor.

The reverse is also becoming relevant. As AI tools demonstrate consistent value in specific domains, with radiology and pathology as leading examples, the standard of care is shifting. A 2026 Medical Economics analysis suggested that in fields where AI use becomes pervasive and demonstrably useful, failing to use it could itself fall below the standard of care. The same technology that creates new liability exposure may also become a baseline expectation.

How Michigan Plaintiffs Approach the Chain

Michigan does not yet have a statute specifically addressing AI in healthcare. Cases are brought under existing frameworks, including the state’s medical malpractice rules, with their procedural requirements for notice and a sworn expert statement, and the state’s product liability law for claims against developers. Because the chain has multiple links, multi-defendant litigation is the norm rather than the exception.

For an injured Michigan patient, that means evidence collection looks different than in a traditional misdiagnosis case. The medical record matters, but so do the algorithmic outputs, the AI vendor’s documentation, the hospital’s policies, and the staff’s training records. Recovering all of that requires a litigation strategy that anticipates the full chain from the start. AI in medicine is here to stay, and so are the injuries that will sometimes follow. The legal system is working out where responsibility sits, but injured patients do not have to wait for that process to finish. The existing law already provides paths to accountability, often against more than one defendant. If you believe AI played a role in a misdiagnosis or treatment failure that harmed you or a loved one, contact Sommers Schwartz for a confidential consultation.