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What risks arise from biased AI algorithms in medical decision-making?

Biased AI in healthcare can perpetuate and amplify existing health disparities with serious consequences. Training data bias occurs when AI learns from datasets underrepresenting certain populations: if developed primarily on data from white males, algorithms may miss disease patterns presenting differently in women or racial minorities. Pulse oximeters using AI algorithms have shown racial bias, overestimating oxygen levels in Black patients—potentially delaying critical interventions. Algorithmic bias in resource allocation has been documented in widely-used systems: one study found algorithms prioritizing white patients over equally-sick Black patients for care management programs due to using healthcare spending (a biased proxy for need) rather than actual health status. Diagnostic accuracy disparities emerge when AI performs well for majority populations but poorly for minorities: dermatology AI trained predominantly on light skin may miss melanoma in dark skin. This could delay cancer detection and worsen mortality gaps. Treatment recommendation bias might suggest different therapies based on demographic factors unrelated to medical appropriateness. Screening and prevention algorithm bias could under-identify high-risk minority patients for preventive interventions. Consequences include: widening health equity gaps, undermined trust in healthcare among already-marginalized communities, potential legal liability, and ethical failures violating principles of justice and non-maleficence. Mitigation requires: diverse training data, fairness testing across demographic groups, transparency about limitations, ongoing monitoring for disparate impact, and human oversight catching biased recommendations before clinical harm occurs.