AI Bias in Medical Diagnoses Raises Malpractice and Discrimination Risks

Artificial intelligence in health care has rapidly evolved from theoretical tools like INTERNIST-1 to modern large language models used in clinical diagnostics, but with increased adoption comes heightened legal and ethical scrutiny. A growing body of evidence shows that medical AI systems may reproduce or even magnify existing biases in health care delivery. From data collection to deployment, biases embedded in AI training datasets—especially around race, gender, and geography—can lead to flawed outputs that disproportionately harm marginalized patient populations.

For insurance claims adjusters, these systemic issues are more than abstract ethical debates—they represent emerging areas of liability. A misdiagnosis stemming from AI bias can trigger not only medical malpractice claims but also civil rights litigation under federal anti-discrimination laws. Recent legal precedents, including the Welch v. United Network for Organ Sharing and Costin v. Glens Falls Hospital cases, demonstrate how biased algorithms are already influencing courtroom decisions and shaping legal interpretations around AI’s role in patient care.

This growing complexity in AI-assisted diagnostics demands heightened vigilance from insurers, especially those handling professional liability, medical malpractice, and civil rights violation claims. Adjusters must evaluate whether AI was involved in diagnostic decisions and scrutinize how bias in algorithmic tools may have contributed to harm. Understanding these dynamics will be critical as courts continue to weigh whether the use of flawed AI systems constitutes mere negligence or crosses into intentional discrimination.