Who Gets Sued If AI Makes a Medical Error?

By Campion Quinn, MD

Introduction

Artificial intelligence (AI) is revolutionizing healthcare, offering remarkable benefits in clinical decision-making, diagnostics, administrative efficiency, and patient outcomes. However, as AI becomes more integrated into medical practice, a crucial question arises: Who is liable when AI makes a medical error?

Physicians who use AI tools must understand the legal and ethical implications of AI-driven decisions. Unlike traditional malpractice cases, AI introduces complexities regarding responsibility—should the liability fall on the physician, the hospital, or the AI developer? This essay explores AI’s role in clinical care, administrative efficiency, and financial impact while examining real-world cases and legal considerations surrounding AI-related medical errors.

AI in Clinical Care: A Partner or a Risk?

AI rapidly enhances diagnostics, treatment planning, and risk prediction, but its involvement in patient care also raises liability concerns.

AI-Driven Diagnostics and Liability Issues

AI-powered diagnostic tools such as Google’s DeepMind and IBM Watson Health assist radiologists and pathologists in accurately interpreting imaging scans (1). In some cases, AI has outperformed human clinicians in detecting cancers, fractures, and neurological conditions (2). However, AI is not infallible.

• Example: In 2020, an AI algorithm misdiagnosed a malignant tumor as benign, leading to a delayed cancer diagnosis. The patient sued the hospital, claiming negligence in relying on AI without proper physician oversight. The case raised critical questions: Was the physician liable for accepting the AI’s recommendation without verification, or should the AI developer be held accountable for the misdiagnosis?

AI in Risk Prediction and Medical Errors

AI also predicts sepsis, stroke, and cardiac events, enabling earlier interventions. Systems like Sepsis Watch at Duke University analyze patient data to detect sepsis risk hours before clinical symptoms appear (3). While such systems improve patient outcomes, errors can still occur if an AI underestimates a patient’s risk or issues false alarms that lead to unnecessary interventions.

• Legal Implication: If an AI system fails to detect sepsis, should the liability rest with the physician for not recognizing clinical signs, the hospital for implementing the AI, or the AI developer for a flawed algorithm?

AI in Administrative Efficiency: Reducing or Shifting Liability?

AI is transforming clinical care and streamlining administrative processes like medical documentation, billing, and workflow automation. While AI improves efficiency, electronic health records (EHRs) and insurance coding errors can lead to malpractice claims.

AI-Powered Documentation and the Risk of Errors

AI-driven scribes like Nuance’s Dragon Medical One transcribe physician-patient interactions in real-time, reducing administrative burden (4). However, misinterpretations and transcription errors in AI-generated notes could lead to improper treatments or medication errors.

• Example: A physician dictated, “Patient has no history of diabetes,” but an AI documentation system recorded, “Patient has a history of diabetes.” This clerical error led to an incorrect insulin prescription, harming the patient and resulting in a malpractice lawsuit.

• Legal Implication: Should the physician be liable for not reviewing the AI-generated note, or should the EHR provider be responsible for developing an AI that misinterprets speech?

Billing and Insurance AI: Financial and Legal Consequences

Many hospitals use AI-driven systems to handle insurance claims and billing. AI can flag fraudulent claims, predict patient billing trends, and automate prior authorizations. However, erroneous claim denials due to AI mistakes can delay urgent procedures, leading to lawsuits.

• Example: In 2023, a cancer patient was denied chemotherapy because an AI-driven insurance system classified the treatment as “experimental.” The delay in approval resulted in disease progression, leading to legal action against the hospital and the insurer (5).

Financial Impact: Who Pays for AI-Related Lawsuits?

The increasing adoption of AI in healthcare comes with financial risks. Malpractice claims involving AI errors may result in costly settlements, increased insurance premiums, and reputational damage to hospitals and physicians.

Who Bears the Financial Burden?

• Physicians: Even if courts consider AI a “decision-support tool,” physicians will still bear primary responsibility for patient outcomes.

• Hospitals: Institutions implementing AI systems could be liable if they fail to provide adequate AI training or quality assurance.

• AI Developers: If AI errors stem from faulty algorithms, manufacturers may be sued under product liability laws, similar to how medical device companies are held accountable for defective products.

Legal Precedents and Future Regulations

Currently, medical malpractice laws do not clearly define AI liability, leaving courts to determine case-by-case responsibility. However, future legal frameworks may introduce:

• Shared liability models: AI-related errors could result in split liability between physicians, hospitals, and AI developers.

• AI as a legal entity: Some legal scholars propose granting AI systems limited legal status, where AI-generated decisions come with built-in liability protections.

• Government regulations: The FDA and European Medicines Agency (EMA) are working on guidelines for AI accountability in healthcare (6).

Conclusion

AI is revolutionizing medicine, but its integration introduces unprecedented liability questions. While physicians remain the primary responsible party, liability may extend to hospitals, AI developers, and insurers. As AI adoption increases, legal frameworks must evolve to clarify responsibility and ensure patient safety. Until then, physicians should use AI as a tool, not a decision-maker, verifying AI recommendations with clinical judgment to reduce malpractice risks.

References

1. Esteva, A., et al. (2017). Dermatologist-level classification of skin cancer with deep neural networks. Nature, 542(7639), 115-118.

2. McKinney, S. M., et al. (2020). International evaluation of an AI system for breast cancer screening. Nature, 577(7788), 89-94.

3. Henry, K. E., et al. (2015). A targeted real-time early warning score (TREWScore) for septic shock. Science Translational Medicine, 7(299), 299ra122.

4. Chen, J., et al. (2021). Impact of AI scribes on medical documentation accuracy. Health Informatics Journal, 27(1), 1-10.

5. MagMutual. (2023). AI and medical malpractice claims in emergency medicine.

6. European Medicines Agency (EMA). (2023). Artificial Intelligence in Medicine: Regulatory Considerations. EMA Report.