AI models miss disease in Black and female patients

Deployment vs. safety and clinical validation

• Some argue AI should still be deployed if it improves outcomes for any group, with careful use (e.g., second reader, not primary diagnosis).
• Others counter that false positives and misuse cause significant harm; broad deployment should wait for robust, evidence-based clinical trials and clear usage guidelines.
• Several note that in practice tools are often sold as replacements for experts, so we must assume “stupid use” will happen unless tightly regulated.

Data bias, representation, and personalized models

• Many comments attribute the disparities to skewed training data: older, white, male patients are overrepresented in the Boston dataset; Black, female, and younger patients are underrepresented.
• Proposed fixes:
  • Larger, more diverse and curated datasets (“DIET”) and fairness-aware training.
  • Including race, sex, age, and even socioeconomic factors as explicit inputs.
  • Separate or specialized models for different subpopulations, framed by some as “personalized medicine” and by others as potentially “separate but equal.”
• Skeptics note that personalized medicine and specialized AI often have unproven real-world benefit and can become justifications for more data extraction and rent-seeking.

Race, sex, age, and what the model is actually learning

• Many are struck that AI can infer race from X-rays even when humans can’t; suggested mechanisms include subtle anatomical differences, environmental effects, or spurious cues (e.g., hospital artifacts).
• One view: because race/sex weren’t provided, the model implicitly learns a “standard” (older white male) and performs worse on others.
• Others suggest the issue could partly be human overdiagnosis in some groups, but this is flagged as unclear from the study.

Fairness, ethics, and politics

• Concern that differing performance by group could fuel political backlash and accusations of preferential treatment, especially if AI is used mainly on one population.
• Debate over fairness vs. utility:
  • Some say deploy if it helps anyone, with clear contraindications and disclosures (“this tool is validated only for group X”).
  • Others emphasize that unequal performance can amplify existing inequities and requires deliberate social and regulatory responses, not just technical tweaks.
• Several point out that biases against women and Black patients are already well documented in human medicine; AI risks amplifying these unless explicitly addressed.

LLMs, “thinking models,” and workflow design

• A linked “MedFuzz” study shows LLMs can be derailed by irrelevant but stereotype-laden details (income, ethnicity, folk remedies), suggesting high susceptibility to biased context.
• Suggested mitigation: a human-led charting/filtering stage before LLM input; AI to assist with summarization and prompting, not raw patient narratives.
• Discussion notes that humans also use heuristics and are biased, but are generally less “suggestible” than current chat-tuned models.

Broader context: systemic bias in medical research

• Multiple comments note long-standing underrepresentation of women and minorities in trials and textbooks (e.g., exclusion due to pregnancy concerns, use of “default” young male subjects).
• This legacy means base medical knowledge and datasets already embed demographic blind spots; AI trained on top of that will naturally inherit them.
• Some argue this is fundamentally a social and institutional problem; technical fixes can help but cannot substitute for broader changes in how medicine is researched and practiced.