AI Misdiagnosis Case Tracker: Diagnostic AI Failures, Lawsuits, and Litigation

The High Stakes of Diagnostic AI

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When artificial intelligence gets a diagnosis wrong, the consequences can be catastrophic. Missed cancers, delayed stroke treatment, sepsis alerts that fail to fire, diagnostic AI failures are increasingly documented, yet lawsuits directly challenging these systems remain rare. This tracker compiles the evidence: validated failures, performance gaps, bias documentation, FDA recalls, and the emerging litigation that will shape AI medical liability for decades.

Radiology AI Failures

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Documented Performance Gaps

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Radiology AI comprises 78% of FDA-cleared AI medical devices, making it the highest-risk category for misdiagnosis. Despite marketing claims of high accuracy, real-world performance varies dramatically.

Critical Incidents:

Radiology AI Misdiagnosis Case Examples

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The following cases illustrate the emerging liability landscape for radiology AI failures. While many involve traditional radiology malpractice, they establish the damages framework AI systems will face as adoption increases.

See also: AI Medical Device Adverse Events, Comprehensive device-level analysis and MAUDE database trends

Pathology AI: Digital Diagnosis Failures

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Emerging Risk Category

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While pathology AI has shown promise:Paige Prostate became the first FDA-approved AI application in pathology in 2021, significant validation gaps remain. Unlike radiology AI with its 182 documented recalls, pathology AI adverse events are less publicly documented, partly because the field is newer and adoption remains limited.

Documented Performance Concerns

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Liability Framework for Pathology AI

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Who Bears Responsibility:

Cardiology AI: ECG and Cardiac Imaging Failures

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The Atrial Fibrillation False Positive Crisis

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AI-powered ECG analysis has exploded in adoption, with CMS including AI-ECG technology in its 2025 Hospital Outpatient Prospective Payment System (OPPS) final rule. But population-scale screening creates massive false positive risks.

The False Positive Cascade

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When AI-ECG systems screen large populations, even high specificity creates massive downstream harm:

The Math (from AAFP 2024):

• Assume 10 million people screened via smartwatch/wearable
• 90% specificity (considered high)
• 2% actual AF prevalence
• Result: 980,000 false positive diagnoses

Consequences of False Positives:

• Iatrogenic harm from unnecessary diagnostic testing (echocardiograms, cardiac catheterization)
• Bleeding complications from unnecessary anticoagulation
• Psychological anxiety from cardiac diagnosis
• Healthcare system burden and costs

Documented Performance Issues

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Positive Developments

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Not all cardiology AI news is concerning. A TCT 2025 study showed AI-ECG reduced false-positive cath lab activations from 41.8% to 7.9% for STEMI detection, a fourfold improvement. This demonstrates that properly validated AI can improve outcomes when targeted at specific, well-defined clinical questions.

See also: Cardiology AI Standard of Care, Full analysis of cardiac AI liability

Emergency Medicine AI: Triage and Diagnostic Failures

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The High-Stakes Environment

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Emergency departments represent perhaps the highest-risk environment for AI diagnostic tools. Chaotic, high-pressure settings with limited information and cognitive overload create fertile ground for both AI benefits and catastrophic failures.

Traditional Triage Failures AI Must Address

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AI Triage Risks

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While AI offers potential solutions, it introduces new failure modes:

“Overconfident Answers”: AI systems may present diagnoses with inappropriate certainty, leading clinicians to accept incorrect recommendations without adequate scrutiny.

Limited Real-World Validation: Most AI emergency medicine research remains retrospective and proof-of-concept. As one systematic review noted: “The potential for AI applications in routine clinical care settings is yet to be achieved.”

Liability Ambiguity: Jurisdictions worldwide are grappling with accountability questions:

• Should providers be held accountable for following AI advice?
• Can liabilities extend to AI developers or institutions?
• The EU AI Act and FDA guidance represent initial steps, but specific guidelines for LLM-driven decision support remain limited.

Emerging Standard of Care Questions

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Mammography AI: Cancer Detection Performance

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AI-STREAM Trial Results (2025)

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The AI-STREAM prospective multicenter cohort study (24,543 women, 140 screen-detected cancers) provides the most rigorous evaluation of AI mammography performance:

Key Finding: While AI-CAD detected some cancers missed by radiologists, it missed more than twice as many that radiologists caught, challenging vendor marketing claims of AI superiority.

Dense Breast Tissue Challenge

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The most common reason for AI-missed cancers was lesions obscured by overlapping dense breast tissue:

• Overall mammographic sensitivity: 75-85% (drops to 30-50% in dense breasts)
• Women with dense breasts (≥75% density) face higher cancer risk AND lower detection rates
• AI systems trained primarily on non-dense breasts perform poorly on this high-risk population

FDA-Cleared Mammography AI

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More than 20 FDA-approved AI applications exist for breast imaging, but adoption remains “widely variable and low overall.” Historical CAD performance issues persist:

“In 2015, researchers demonstrated that although FDA had long cleared CAD for clinical use, CAD didn’t improve radiologists’ interpretations of mammograms in routine practice. In fact, CAD decreased sensitivity in the subset of radiologists who interpreted studies with and without it.”

AI Malpractice Claims: 2024-2025 Trends

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Emerging Litigation Statistics

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Primary AI Malpractice Sources: The majority of AI-related malpractice claims stem from diagnostic AI in:

1. Radiology (imaging interpretation)
2. Cardiology (ECG analysis)
3. Oncology (treatment recommendations)

Insurance Industry Response

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Malpractice insurers are adapting to AI risks:

• Some insurers have introduced AI-specific exclusions
• Others require physicians to complete AI training to maintain coverage
• Premium adjustments for facilities deploying unvalidated AI
• New policy language addressing “algorithm error” vs “physician error”

Regulatory Developments

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Federation of State Medical Boards (April 2024): Suggested member boards hold clinicians, not AI makers, liable when AI makes medical errors, placing documentation and validation burden on physicians.

Georgia (2024): First state to pass legislation specifically governing AI in healthcare.

Texas AG (September 2024): First enforcement action against AI healthcare vendor for deceptive accuracy claims.

The Radiologist Liability Trap

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AI creates a novel double-bind for radiologists:

If AI flags something the radiologist misses:

“If AI flags a lung nodule on a chest radiograph that the radiologist doesn’t see and therefore doesn’t mention in the report, and that nodule turns out to be cancerous, the radiologist may be liable not just for missing the cancer but for ignoring AI’s advice.”

If radiologist follows AI and it’s wrong: The physician may be liable for failing to apply independent clinical judgment.

See also: AI Medical Device Adverse Events, Comprehensive device-level analysis

Epic Sepsis Model: The Most Documented Failure

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The Performance Crisis

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The Epic Sepsis Model (ESM) is deployed at hundreds of US hospitals. A landmark JAMA Internal Medicine study exposed catastrophic underperformance:

Why It Failed

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Training vs Reality: The model was trained on synthetic sepsis definitions that don’t match real-world clinical presentations. When validated against Medicare/CDC-aligned definitions, performance collapsed.

No Independent Validation: Hundreds of hospitals deployed the algorithm without verifying its advertised 80% accuracy rate.

Epic’s Response: Epic disputed the findings, claiming hospitals needed to “tune” the model before deployment. In 2022, Epic released an updated version claiming better performance, but independent validation remains limited.

Liability Implications

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Hospitals deploying unvalidated AI for sepsis detection face potential liability for:

• Negligent implementation of unvalidated clinical tools
• Corporate negligence for systemic failure to validate vendor claims
• False Claims Act exposure if billing for AI-enhanced care that doesn’t meet standards

Dermatology AI: Racial Bias Crisis

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Documented Disparities

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Dermatology AI demonstrates some of the most severe documented racial bias in medical AI:

Performance Gap Evidence

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Northwestern University Study (2024):

• AI assistance improved diagnostic accuracy 33% for dermatologists, 69% for primary care
• However: Accuracy gap between light and dark skin tones widened with AI
• Primary care physicians who see mostly white patients showed AI-exacerbated bias on dark skin

Training Data Problem:

• Medical textbooks and dermatology training materials lack darker skin tone examples
• AI systems trained on unrepresentative data systematically misdiagnose conditions in darker skin
• Skin cancer detection models trained only on lighter skin perform poorly on darker-skinned patients

Potential Solutions

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Research shows fine-tuning AI models on diverse datasets (like the DDI dataset) effectively closes the performance gap, but most commercial tools haven’t implemented these corrections.

IBM Watson for Oncology: The $4 Billion Failure

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What Went Wrong

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IBM Watson for Oncology was marketed as revolutionary AI that would transform cancer treatment. Instead, it became a cautionary tale of AI overpromise.

Documented Failures:

Legal and Liability Framework

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Legal scholars have debated Watson’s liability status:

Arguments for AI Personhood: Some suggest Watson warrants status analogous to a medical resident, requiring oversight but bearing some responsibility.

Current Reality: Because Watson was part of a physician team, it was never solely responsible for injuries. Each treating physician remains liable for their portion of damages.

The Outcome: IBM sold Watson Health’s data and analytics products for over $1 billion to Francisco Partners, a fraction of the $4 billion invested in development.

FDA Recalls and Safety Signals

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Recall Statistics (Through November 2024)

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A JAMA study analyzed FDA recalls of AI-enabled medical devices:

Recall Causes:

Validation Gap Problem

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The FDA’s 510(k) clearance pathway, used for 97% of AI medical devices, doesn’t require prospective human testing:

• Devices enter market with limited or no clinical evaluation
• AI lacking validation data before FDA clearance is more likely to be recalled
• Even devices with strong premarket data “frequently” perform worse in real-world settings
• Public company devices recalled more frequently (92%) than private company devices (53%)

See also: AI Medical Device Adverse Events, MAUDE database analysis and FDA reporting gaps

Malpractice Verdicts and Settlements

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Cancer Misdiagnosis (2024)

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While not exclusively AI-related, radiology malpractice verdicts establish the liability framework AI systems will face:

Average Values:

• Cancer misdiagnosis settlements average $300,000-$660,000
• 43% of breast cancer misdiagnosis defendants are radiologists

Emerging AI-Specific Litigation

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Direct AI diagnostic malpractice lawsuits remain rare, but the foundation is being established:

Theoretical Framework:

• Product liability claims against AI developers for design defects
• Malpractice claims against physicians for over-reliance on flawed AI
• Hospital negligence for deploying unvalidated AI systems
• Corporate manslaughter theories for gross negligence (UK precedent emerging)

Emerging Litigation Trends

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Product Liability for Diagnostic AI

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Following the Garcia v. Character Technologies precedent (treating AI as a “product”), diagnostic AI developers may face:

Design Defect Claims:

• AI trained on biased data
• Inadequate validation across patient populations
• Failure to perform as marketed

Failure to Warn:

• Inadequate disclosure of accuracy limitations
• Missing warnings about demographic performance gaps
• No disclosure of known failure modes

Manufacturing Defect:

• Training data contamination
• Version-specific bugs
• Data drift causing degraded performance

Hospital and Health System Liability

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Healthcare organizations deploying AI face potential claims for:

Negligent Selection:

• Choosing AI vendors without validating claims
• Deploying systems with known bias issues
• Ignoring FDA recall notices

Negligent Implementation:

• Failure to customize AI for local patient populations
• Inadequate training for clinical staff
• No override protocols for AI recommendations

Corporate Negligence:

• Systemic failure to monitor AI outcomes
• Prioritizing efficiency over patient safety
• Suppressing internal concerns about AI performance

Standard of Care for Diagnostic AI

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What Reasonable Use Looks Like

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Based on FDA guidance and emerging best practices:

Pre-Deployment:

• Independent validation in local patient population
• Bias testing across demographics
• Clear performance benchmarks vs human decision-making
• Override protocols for AI recommendations

Operational:

• Human review of all AI diagnostic recommendations
• Documentation of when AI is followed vs overridden
• Outcome monitoring by patient demographics
• Alert fatigue management

Ongoing:

• Regular revalidation as patient populations change
• Tracking real-world performance vs marketed claims
• Reporting to FDA when performance degrades
• Updating based on new evidence

What Falls Below Standard

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Practices likely to support liability:

• Deploying AI without independent validation
• Using AI with known demographic performance gaps
• Following AI recommendations without clinical judgment
• Ignoring FDA recalls or safety signals
• Failing to track outcomes
• Over-relying on vendor marketing claims

Frequently Asked Questions

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Related Resources

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AI Liability Framework

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• AI Product Liability, Strict liability for AI systems
• AI Software as a Product, Garcia ruling analysis
• AI Litigation Landscape 2025, Overview of AI lawsuits

Healthcare AI

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• AI Medical Device Adverse Events, FDA reporting and device-level analysis
• Healthcare AI Standard of Care, Medical AI deployment standards
• Radiology AI Standard of Care, Imaging AI liability framework
• Cardiology AI Standard of Care, Cardiac AI liability framework
• Surgical Robotics Standard of Care, Robotic surgery liability
• AI Insurance Claim Denials, Health insurer AI lawsuits

Algorithmic Bias

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• Mobley v. Workday, AI discrimination class action
• AI Family Law and Custody, Algorithm bias in sensitive decisions