Impact of Demographics on AI Diagnosis

Changing the demographics of patient cases significantly impacts AI models' diagnostic accuracy, as demonstrated in a study where swapping race or gender in medical scenarios affected correct diagnoses in 37% of evaluated cases. A striking example arose from a case of pharyngitis in sexually active teenagers; while AI correctly diagnosed mono in 100% of white patients, it only achieved a 64 to 84% accuracy in minority patients, often leading to misdiagnoses related to STDs. This highlighted the reliance of AI systems on historical biases present in their training data, ultimately affecting their performance on diverse populations. Such disparities suggest a pressing need to address how AI tools are trained and utilized, particularly in sensitive healthcare contexts where accuracy is critical for proper care.