A new perspective paper highlights significant biases in biomedical AI, stemming from data collection and research prioritization rather than just clinical implementation. Analysis of omics publications and large datasets shows limited reporting of demographic information, with European-ancestry data dominating. The paper warns that as biomedical foundation models become more prevalent, they risk perpetuating these early-stage biases, leading to cascading healthcare inequities. The authors propose a focus on provenance, openness, and reliability through evaluation transparency to mitigate these issues. AI
IMPACT Biomedical AI models risk perpetuating healthcare disparities due to biased data, necessitating a focus on data provenance and transparency.
RANK_REASON The cluster contains an academic paper discussing AI research and its implications. [lever_c_demoted from research: ic=1 ai=1.0]
AI-generated summary · Google Gemini · from 1 sources. How we write summaries →