Lawyers estimate settlement value by weighing provable harm against the chances of establishing fault, medical causation, and admissible expert evidence. Trial risk rises when responsibility is divided among clinicians, healthcare organizations, and the software developer. No specific tool, patient, or court record is identified here, so a particular AI-caused missed diagnosis cannot be verified. Stanford HAI reports that healthcare-AI injury case law remains sparse, with few personal-injury claims producing judicial opinions.
Official resources:
- Read the official guidance from FDA — Use this primary source to verify the official guidance.
- Read the original report from Gao — Use this primary source to review the complete report.
Table of Contents
- What determines settlement value?
- Who may be legally responsible?
- Why the tool's intended use matters
- What makes trial especially risky?
- Evidence that can change the evaluation
What determines settlement value?
The starting point is the injury caused by the diagnostic delay, not merely the software's mistake. Lawyers compare the patient's likely outcome with a timely diagnosis against the actual course of illness, treatment, disability, and financial loss. A severe outcome does not automatically create a strong claim.
The evidence must connect the missed diagnosis to additional harm. If the condition would probably have progressed despite timely care, the case may have substantial damages but weak causation. Lawyers commonly evaluate:.
- The seriousness and permanence of the added injury
- The medical evidence supporting an earlier diagnosis
- Whether timely treatment likely would have improved the outcome
- The strength and cost of expert testimony
- Available defendants, insurance, and practical sources of recovery
Who may be legally responsible?
A claim against a clinician or healthcare organization generally requires proof of duty, breach of the standard of care, and causation. In AI-assisted care, Stanford HAI identifies a central question: was it unreasonable for the clinician to follow or reject the model's recommendation? Stanford HAI's healthcare-AI liability brief Consider a tool that marks a scan as low risk despite visible warning signs. The case against the clinician becomes stronger if a reasonably careful review should have caught those signs.
It becomes weaker if the failure was undetectable without technical information controlled by the developer. A developer claim can add another potential source of recovery, but also more legal uncertainty. Courts have sometimes resisted treating intangible software like a conventional product. Some FDA-authorized devices may present federal preemption questions, and a design claim may require proof of a feasible safer alternative.
Why the tool's intended use matters
Lawyers must determine exactly what the software was designed to do. A screening aid, prioritization system, diagnostic recommendation, and autonomous detection tool may create different expectations for users. FDA's January 2026 guidance distinguishes certain clinical-decision-support functions excluded from the statutory definition of a medical device from software functions that remain regulated devices.
That makes the product's intended use and regulatory status important to both liability analysis and discovery. FDA guidance on clinical decision support software The practical inquiry compares the tool's documented limits with its actual use. Relevant questions include whether the patient belonged to the validated population, whether the correct inputs were supplied, and whether warnings required independent clinical review.
What makes trial especially risky?
Expert testimony may decide whether the patient can connect the software output, the clinician's response, and the eventual injury. In federal court, Rule 702 requires expert opinions to rest on sufficient facts or data, reliable methods, and reliable application to the case. Federal Rule of Evidence 702 That creates several failure points. A medical expert may establish that earlier treatment mattered but lack expertise in model performance.
A technical expert may explain the system but be unable to prove what a clinician should have done. Historical malpractice data also illustrates the settlement pressure created by trial uncertainty. In a study of 1,452 closed claims, plaintiffs received compensation in 61% of out-of-court resolutions but won only 21% of trial verdicts; average defense costs were also substantially higher at trial. Those figures are dated and not specific to AI disputes, but they show why both sides discount uncertain cases. The New England Journal of Medicine malpractice-claims study.
Evidence that can change the evaluation
The most useful evidence often shows what information existed when the diagnosis was missed. Later summaries cannot fully replace the original medical images, software output, audit trail, or clinician notes. A patient or lawyer should identify and preserve: Potential claimants should request preservation of relevant electronic records before routine system changes or retention practices make the original output harder to reconstruct.
- The original scans, laboratory results, and clinical records
- The software's output, score, alert, or recommendation
- The product name, version, and configuration used
- Instructions, warnings, and stated limitations
- Records showing who reviewed or overrode the output