Year: 2021
Type: review
Abstract: 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.
Source: EBioMedicine
Authors: James Zou, Londa Schiebinger
Institution: Stanford University
Language: English
Subfield: Health Informatics
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $3,500
Awards: AF: MEDIUM: Collaborative Research: Foundations of Adaptive Data Analysis, G4903437645, CAREER: Enabling data valuation and deletion in human-centered machine learning, G6671297155, Role of Single Cell mRNA Variation in Systems Associated Electrically Excitable C, Stanford Medicine Center for Longevity and Healthy Aging
OpenAlex ID: W3158393586