Stigma, biomarkers, and algorithmic bias: recommendations for precision behavioral health with artificial intelligence

Author:

Walsh Colin G1,Chaudhry Beenish2,Dua Prerna3,Goodman Kenneth W4,Kaplan Bonnie5,Kavuluru Ramakanth6,Solomonides Anthony7,Subbian Vignesh8

Affiliation:

1. Biomedical Informatics, Medicine and Psychiatry, Vanderbilt University Medical Center, 2525 West End, Suite 1475, Nashville, TN, USA

2. School of Computing and Informatics, University of Louisiana at Lafayette, Lafayette, Louisiana, USA

3. Department of Health Informatics and Information Management, Louisiana Tech University, Ruston, Louisiana, USA

4. Institute for Bioethics and Health Policy, University of Miami, Miller School of Medicine, Miami, Florida, USA

5. Yale Center for Medical Informatics, Yale Bioethics Center, Yale Information Society, Yale Solomon Center for Health Law & Policy, Yale University, New Haven, Connecticut, USA

6. Division of Biomedical Informatics, Department of Internal Medicine, University of Kentucky, Lexington, Kentucky, USA

7. Outcomes Research and Biomedical Informatics, NorthShore University HealthSystem, Research Institute, Evanston, Illinois, USA

8. Department of Biomedical Engineering, Department of Systems and Industrial Engineering, The University of Arizona, Tucson, Arizona, USA

Abstract

Abstract Effective implementation of artificial intelligence in behavioral healthcare delivery depends on overcoming challenges that are pronounced in this domain. Self and social stigma contribute to under-reported symptoms, and under-coding worsens ascertainment. Health disparities contribute to algorithmic bias. Lack of reliable biological and clinical markers hinders model development, and model explainability challenges impede trust among users. In this perspective, we describe these challenges and discuss design and implementation recommendations to overcome them in intelligent systems for behavioral and mental health.

Funder

National Institute of General Medical Sciences

National Institutes of Health

U.S. National Center for Advancing Translational Sciences

National Science Foundation

Publisher

Oxford University Press (OUP)

Subject

Health Informatics

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