AI in Biomedicine—A Forward-Looking Perspective on Health Equity

Deepak Kumar (), Bradley A. Malin, Jamboor K. Vishwanatha, Lang Wu and Jerris R. Hedges ()
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Deepak Kumar: The Julius L. Chambers Biomedical/Biotechnology Research Institute (JLC-BBRI), Department of Pharmaceutical Sciences, North Carolina Central University (NCCU), Durham, NC 27707, USA
Bradley A. Malin: Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN 37232, USA
Jamboor K. Vishwanatha: Institute for Health Disparities, University of North Texas Health Science Center, Fort Worth, TX 76107, USA
Lang Wu: University of Hawai’i Cancer Center, University of Hawai’i at Mānoa, Honolulu, HI 96813, USA
Jerris R. Hedges: Department of Surgery, John A. Burns School of Medicine, University of Hawai’i at Mānoa, Honolulu, HI 96813, USA

IJERPH, 2024, vol. 21, issue 12, 1-8

Abstract: As new artificial intelligence (AI) tools are being developed and as AI continues to revolutionize healthcare, its potential to advance health equity is increasingly recognized. The 2024 Research Centers in Minority Institutions (RCMI) Consortium National Conference session titled “Artificial Intelligence: Safely, Ethically, and Responsibly” brought together experts from diverse institutions to explore AI’s role and challenges in advancing health equity. This report summarizes presentations and discussions from the conference focused on AI’s potential and its challenges, particularly algorithmic bias, transparency, and the under-representation of minority groups in AI datasets. Key topics included AI’s predictive and generative capabilities in healthcare, ethical governance, and key national initiatives, like AIM-AHEAD. The session highlighted the critical role of RCMI institutions in fostering diverse AI/machine learning research and in developing culturally competent AI tools. Other discussions included AI’s capacity to improve patient outcomes, especially for underserved communities, and underscored the necessity for robust ethical standards, a diverse AI and scientific workforce, transparency, and inclusive data practices. The engagement of RCMI institutions is critical to ensure practices in AI development and deployment which prioritize health equity, thus paving the way for a more inclusive AI-driven healthcare system.

Keywords: artificial intelligence; augmented intelligence; machine learning; RCMI; health ethics; health equity; health disparities (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2024
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