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Biomedical AI biased by data, risking healthcare disparities

By PulseAugur Editorial · [1 sources] ·

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]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Biomedical AI biased by data, risking healthcare disparities

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The cluster contains an academic paper discussing AI research and its implications. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Michal Rosen-Zvi, Yoav Kan-Tor, Michael Danziger, Agata Ferretti, Javier Aula-Blasco, Julia Falcao, Ron Shamir, Mira Marcus-Kalish, Mordechai Muszkat ·

    Perspective on Bias in Biomedical AI: Preventing Downstream Healthcare Disparities

    arXiv:2604.14514v2 Announce Type: replace Abstract: Healthcare disparities persist across socioeconomic boundaries, often attributed to unequal access to screening, diagnostics, and therapeutics. However, this perspective highlights that critical biases can emerge much earlier, d…

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