Work

Bias in AI-guided management of patients with major depressive disorders

Year: 2025

Type: article

Abstract: This study reports the extent of bias, and necessary corrections, in an AI system designed to guide management of Major Depressive Disorder. A previous general-population, race-blind, study had identified 1499 medical history risk factors for predicting response to antidepressants. These risk factors included 700 diagnoses, 550 current medications, 151 medical procedures, and 98 aspects of previous antidepressant use. We used hierarchical analysis to examine whether additional race-specific risk factors should be considered when predicting antidepressant response for African Americans. Depressed African Americans in our study took 1.74 different antidepressants during the study periods. In all 14 antidepressants, response to antidepressants for African Americans was better predicted, if we used race-specific models. The largest improvement in variation explained came in predicting response to Amitriptyline, Mirtazapine, and Nortriptyline. These data suggest clinicians should not rely on their experiences with the general population to prescribe medications for African Americans with Major Depressive Disorder.

Source: Journal of Health Equity

DOI: 10.1080/29944694.2025.2606724

Authors: Farrokh Alemi, Kevin Lybarger, Niloofar Ramezani, Vladimir Cardenas, Maria Kurian, Rachael Christine King

Institutions: George Mason University, Virginia Commonwealth University

Language: English

Topic: Treatment of Major Depression

Subfield: Pharmacology

Field: Medicine

Domain: Health Sciences

SDGs: Good health and well-being, Reduced inequalities

Open Access status: diamond

Funders: Patient-Centered Outcomes Research Institute, Artificial Intelligence/Machine Learning Consortium to Advance Health Equity and Researcher Diversity