A widely used healthcare algorithm underestimated the medical needs of Black patients because it used healthcare spending as a proxy for health status¶

Claim · All claims
Evidence · 1 study · review r2 · q2
Currency · newest study 2025 · not yet checked against its sources · not reviewed by a person · recheck by 2028: tool and model accuracy changes quickly

Subclaims¶

q2 i? The review reports, citing Obermeyer et al., that a widely used healthcare algorithm systematically disadvantaged Black patients, and that revising it to use actual health outcomes significantly mitigated the disparity. → Papaneophytou 2025

Evidence¶

Papaneophytou 2025¶

Papaneophytou, C.; Nicolaou, S.A. (2025). Promoting Critical Thinking in Biological Sciences in the Era of Artificial Intelligence: The Role of Higher Education. Trends High. Educ. 2025, 4, 24. https://doi.org/10.3390/higheredu4020024

q2 · i? · review · r2

Narrative review section on AI bias, reporting the Obermeyer et al. [48] finding that fewer Black patients were flagged for follow-up care and that revising the algorithm to base predictions on actual health outcomes significantly mitigated the disparity.

"The model used healthcare spending as a proxy for health status, which systematically disadvantaged patients from communities that historically receive less care."

Discussion¶

Related Claims¶

• Healthcare dominates citation impact in practice-based AI ethics research, while education contributes disproportionately to interpretive debates despite fewer publications — related
• AI output errors are frequent: 44.5% fabricated references in drafted research proposals and 5–13% unsafe chatbot medical answers — related
• AI literacy and formal AI training remain low among nursing and health-professions students, though attitudes are largely positive — related