Perspective on Bias in Biomedical AI: Preventing Downstream Healthcare Disparities
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, during data collection and research prioritization, long before clinical implementation, particularly in studies focused on molecular and omics data. A vast number of studies focus on collecting omics data, but the demographic information associated with these datasets is often not reported, and when it is reported, it reveals substantial biases. An automated analysis of 4514 PubMed-indexed omics publications from 2015 to 2024, examining reporting across multiple de...
| Authors | Michal Rosen-Zvi, Yoav Kan-Tor, Michael Danziger et al. |
|---|---|
| Primary category | cs.AI |
| Published | 16 April 2026 |
| Last updated at source | 31 May 2026 |
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