Ensuring that biomedical AI benefits diverse populations
Open accessAbstract
Artificial Intelligence (AI) can potentially impact many aspects of human health, from basic research discovery to individual health assessment. It is critical that these advances in technology broadly benefit diverse populations from around the world. This can be challenging because AI algorithms are often developed on non-representative samples and evaluated based on narrow metrics. Here we outline key challenges to biomedical AI in outcome design, data collection and technology evaluation, and use examples from precision health to illustrate how bias and health disparity may arise in each stage. We then suggest both short term approaches-more diverse data collection and AI monitoring-and longer term structural changes in funding, publications, and education to address these challenges.
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Diversity of biomedical datasets is paramount as it constitutes the first step to ensure generalizability to the broader population.
“However, diversity of biomedical datasets is paramount as it constitutes the first step to ensure generalizability to the broader population 145”
From Multimodal biomedical AI (2022) · cites this paper as [145]
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Self-citationRetracted
- Authors
- Zou, J., & Schiebinger, L.
- Affiliations
- Stanford University (US)
- Corresponding author
- Schiebinger, Londa
- Venue
- EBioMedicine
- Publisher
- Elsevier BV
- Published
- 2021-05
- Volume
- 67
- Pages
- 103358
- ISSN
- 2352-3964 (ISSN-L)
- PMC
- PMC8176083
- OpenAlex
- W3158393586
- Cited by
- 124
- References
- 60
- Citations by year
- 2026: 16 · 2025: 43 · 2024: 23 · 2023: 17 · 2022: 22 · 2021: 3
- FWCI
- 1.32
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- EBioMedicine (publishedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Science Foundation (1942926)
- National Institutes of Health (P30AG059307, U01MH098953)
- National Science Foundation Directorate for Engineering
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- algorithmic bias, health disparities, underrepresented groups, precision health, data collection, AI evaluation metrics, AI monitoring, research funding
- MeSH terms
- Artificial Intelligence, Health Policy, Humans, Medical Informatics, Health Status Disparities
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