Stigma, biomarkers, and algorithmic bias: recommendations for precision behavioral health with artificial intelligence
| Autor: | Anthony Solomonides, Beenish M. Chaudhry, Prerna Dua, Colin G. Walsh, Vignesh Subbian, Ramakanth Kavuluru, Kenneth W. Goodman, Bonnie J. Kaplan |
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| Rok vydání: | 2020 |
| Předmět: |
Social stigma
business.industry precision medicine Intelligent decision support system health disparities algorithms mental health Health Informatics artificial intelligence Precision medicine ethics Mental health behavioral health Health equity 03 medical and health sciences 0302 clinical medicine Perspective Behavioral healthcare Model development 030212 general & internal medicine Artificial intelligence business Psychology predictive modeling 030217 neurology & neurosurgery |
| Zdroj: | JAMIA Open |
| ISSN: | 2574-2531 |
| DOI: | 10.1093/jamiaopen/ooz054 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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