Who Is Liable When AI Misses a Finding? The New Malpractice Frontier in Radiology

A radiologist reads a chest X-ray and calls it normal. The AI flagged a subtle nodule in the right lower lobe at 87% confidence. The radiologist reviewed the flag, considered it a false positive based on the patient’s history, and moved on. Fourteen months later, that nodule is a stage IIIA lung cancer. The patient’s attorney now has a timestamped record showing that the AI detected the finding and the radiologist dismissed it.

This is not a hypothetical. This is the malpractice landscape that radiology AI malpractice attorneys are preparing for right now.

The adoption of AI in diagnostic radiology has created a new category of medicolegal questions that existing malpractice frameworks were never designed to answer. Who is liable when AI misses a finding? Who is liable when AI catches a finding and the radiologist overrides it? What standard of care applies when AI is available but not used? And how do you document any of this in a way that protects your practice?

The answers are evolving, and every radiologist, practice manager, and hospital administrator needs to understand where the lines are being drawn.

The Liability Landscape in 2026

Malpractice law in radiology has always rested on a straightforward foundation: the radiologist owes a duty of care to the patient, the standard of care is defined by what a reasonably competent radiologist would do, and a breach of that standard that causes harm creates liability. AI complicates every element of that framework.

The standard of care is shifting. As AI tools become more widely available and clinically validated, the question is no longer whether a radiologist should use AI — it is whether failing to use available AI constitutes a departure from the standard of care. Legal scholars and radiology leaders have been raising this question since at least 2023. The American College of Radiology has acknowledged that AI decision support tools are becoming part of the standard workflow, and courts will eventually take notice.

We are not there yet. No court has held that failing to use AI in radiology is malpractice. But the trajectory is clear. When a tool that costs less than a dollar per analysis can catch findings that fatigue causes human readers to miss, it becomes harder to argue that ignoring that tool is reasonable.

Juries are already paying attention. A widely cited study from Brown University examined how mock juries evaluate radiologist liability when AI is involved. The findings were striking: juries judged radiologists significantly more harshly when the radiologist disagreed with an AI system that correctly identified an abnormality. The reasoning, from juror interviews, was intuitive if legally imprecise — “the computer caught it, why didn’t the doctor?” This creates an asymmetric liability risk. When AI agrees with the radiologist, it reinforces the read. When AI disagrees and the radiologist is wrong, the AI output becomes evidence against them.

Claims are rising. Malpractice claims involving AI-assisted medical tools increased approximately 14% between 2022 and 2024, according to data from medical malpractice insurance carriers. This growth reflects both the increasing deployment of AI in clinical settings and the legal profession’s growing awareness of AI as a factor in medical error cases.

Three Liability Scenarios Every Radiologist Should Understand

The radiology AI malpractice landscape is not a single question. It is at least three distinct scenarios, each with different liability implications.

Scenario 1: AI Misses a Finding

The AI analyzes a chest X-ray and returns a negative result. The radiologist, relying in part on the AI output, also reads the study as normal. A finding is later discovered.

Who is liable? Under current law, the radiologist remains liable. AI-assisted analysis is decision support, not autonomous diagnosis. The physician of record bears responsibility for the final interpretation, regardless of whether an AI tool was used. The AI’s miss does not transfer liability away from the radiologist — just as a CAD system missing a finding on mammography does not absolve the reading radiologist.

What about the AI vendor? Product liability claims against AI vendors are possible but face significant hurdles. Most AI systems are cleared as clinical decision support tools under FDA 510(k), and their labeling explicitly states they do not provide diagnoses. Plaintiffs would need to demonstrate that the product was defective, not merely that it failed to detect a finding in a specific case. Given that no diagnostic tool — human or AI — achieves 100% sensitivity, this is a high bar.

The documentation question: Was the AI output reviewed? Was the radiologist’s reasoning for the final interpretation documented? If the AI returned a negative result and the radiologist also read the study as negative, the documentation may be straightforward. The risk increases when there is no record of the AI interaction at all.

Scenario 2: AI Catches a Finding and the Radiologist Overrides It

This is the scenario that keeps malpractice attorneys interested and radiologists up at night. The AI flags a finding. The radiologist reviews the flag, considers it a false positive, and does not include it in the final report. The finding is later confirmed as a true positive.

Who is liable? The radiologist. But this scenario is significantly more dangerous than Scenario 1 because the AI’s output creates a documented record that the finding was identified and then dismissed. The Brown University mock jury study demonstrated that this specific situation — radiologist overriding a correct AI flag — produces the harshest jury reactions.

The clinical reality: Radiologists override AI all the time, and they should. AI systems produce false positives. A system with 95% specificity on a given pathology will generate false positive flags on 5% of negative studies. In a high-volume practice reading 100 chest X-rays per day, that is five false positive flags every day for a single pathology. Experienced radiologists use clinical context, prior studies, and pattern recognition to filter these flags. Most overrides are correct.

The legal problem: Juries do not think in terms of sensitivity and specificity. They think in terms of “the computer said something was wrong and the doctor ignored it.” The radiologist’s clinical reasoning for the override — which may have been entirely sound — needs to be documented clearly enough to withstand post-hoc scrutiny.

Scenario 3: AI Was Available but Not Used

A hospital has an AI second reader system installed and available. A radiologist reads a study without activating or reviewing the AI output. A finding is missed. The plaintiff argues that the radiologist departed from the standard of care by not using available AI.

Who is liable? This is the least-settled scenario. Currently, there is no legal precedent establishing that failing to use available AI constitutes malpractice. But the argument is straightforward and will eventually be tested: if a tool that improves diagnostic accuracy is readily available at minimal cost, and a physician chooses not to use it, does that choice constitute negligence?

The parallel to mammography CAD is instructive. When computer-aided detection became widely available for mammography screening, the question of whether failing to use it constituted malpractice was debated. The resolution was largely institutional — most screening programs adopted CAD as standard practice, and the question became moot. The same pattern is likely for AI in radiology more broadly.

Documentation as Defense

Every malpractice attorney will tell you the same thing: if it is not documented, it did not happen. In the context of AI liability radiology, documentation becomes even more critical because the AI itself creates a record.

What needs to be documented:

• AI output acknowledgment. The radiologist reviewed the AI findings. This should be part of the standard workflow, not an optional step.
• Override rationale. When the radiologist disagrees with an AI flag, the reasoning should be documented. “Reviewed AI finding of possible nodule RLL. Correlates with known granuloma on prior CT 2024-03-15. No follow-up recommended.” This takes thirty seconds and may save a career.
• System availability. Institutional records should track when AI systems are available, when they are down for maintenance, and when individual studies were or were not processed by AI.
• Version and configuration. Which AI version analyzed the study? What pathologies was it configured to detect? This information matters if the AI’s performance is questioned after the fact.

The audit trail advantage: This is where purpose-built clinical AI platforms provide a meaningful advantage over ad hoc tools. AI Bharata’s MYAIRA AI generates a timestamped, immutable record of every analysis — which study was analyzed, what findings were detected, what confidence scores were assigned, and when the results were delivered. Combined with Medixshare’s secure sharing audit trail, which logs every access event, every share, and every view of a shared study, the complete chain of custody for an imaging study is documented from acquisition through AI analysis through specialist consultation.

This audit trail serves two purposes. It protects the radiologist by providing evidence that AI was used, reviewed, and considered. And it protects the institution by demonstrating a systematic, documented approach to AI-assisted care that meets evolving compliance standards.

The EU AI Act and Regulatory Compliance

The regulatory environment for AI liability in radiology is tightening. The European Union’s AI Act, which takes effect in 2026, classifies medical imaging AI as high-risk and imposes specific obligations:

• Transparency requirements. Healthcare providers must be able to explain how AI contributed to a clinical decision.
• Bias testing and monitoring. AI systems must be validated for performance across demographic groups, and ongoing monitoring is required.
• Human oversight mandates. High-risk AI systems must include mechanisms for meaningful human review — precisely the second reader model that AI Bharata’s MYAIRA and other clinical AI platforms employ.
• Incident reporting. Serious AI-related incidents must be reported to regulatory authorities.

While the EU AI Act applies directly to European deployments, its influence is global. US regulatory frameworks are evolving in the same direction, with the FDA’s 2024 guidance on AI lifecycle management and the growing emphasis on real-world performance monitoring.

For practices and hospitals, compliance with these frameworks is not just a regulatory obligation — it is a malpractice defense. Demonstrating that your AI deployment meets recognized regulatory standards strengthens the argument that you met the standard of care.

Practical Steps for Radiology Practices

The malpractice implications of AI in radiology are real but manageable. Here is what practices should be doing now:

1. Establish AI workflow protocols. Define how AI output is integrated into the reading workflow. Is it always reviewed? Is it automatically attached to the study? Who is responsible for ensuring the AI ran on every relevant case? These protocols should be written, distributed, and auditable.

2. Train on override documentation. Radiologists need specific guidance on how to document overrides. Brief, clinical, and contemporaneous is the standard. The override note should reference the AI finding, state the clinical reasoning for disagreement, and cite supporting evidence (prior imaging, clinical history).

3. Choose AI platforms with built-in audit trails. Not all AI systems are equal in their documentation capabilities. A system that logs analyses, findings, confidence scores, and timestamps in an immutable record — and makes that record accessible for legal and compliance review — is significantly more defensible than one that produces findings without a persistent record.

4. Maintain version documentation. Track which AI version is deployed, when it was updated, and what its validated performance characteristics are. If an AI system is upgraded and its detection profile changes, that transition should be documented.

5. Engage with your malpractice insurer. Most medical malpractice insurers are actively developing policies around AI use. Some offer premium adjustments for practices that use AI with documented protocols. Contact your insurer proactively and ask about their AI-specific guidance.

6. Consult legal counsel. The law in this area is evolving rapidly. A healthcare attorney familiar with AI-related medical malpractice can review your protocols and identify gaps before they become liabilities.

The Emerging Standard

The malpractice landscape for radiology AI is not static. It is converging toward a standard that will likely include the following expectations:

• AI-assisted analysis is available and integrated into the diagnostic workflow as standard practice.
• AI output is reviewed and documented as part of every relevant interpretation.
• Overrides of AI findings are documented with clinical reasoning.
• Audit trails maintain a complete record of AI-human interaction for every study.
• Institutions demonstrate compliance with applicable regulatory frameworks.

Practices that establish these protocols now are not just managing liability risk — they are building the documentation infrastructure that will define responsible AI use in radiology for the next decade.

The question is no longer whether AI will change the malpractice landscape in radiology. It already has. The question is whether your practice is documenting its way through the transition — or leaving a paper trail that a plaintiff’s attorney will exploit.

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Protect your practice with AI that documents everything. MYAIRA AI provides timestamped analysis with immutable audit trails for every study. Explore MYAIRA for hospitals or see how our features support compliance. Need to share scans securely with documented chain of custody? Learn about Medixshare.