HMN 2026: What are the pitfalls of one-size-fits-all AI mental health treatment

After developing an AI tool to recommend antidepressants based on medical history, George Mason University researchers are now examining whether additional patient demographics, such as race and ethnicity, can improve the tool’s effectiveness. The answer is yes, according to their new research.

The study is published in the Journal of Health Equity.

An interdisciplinary George Mason University team led by Farrokh Alemi, an expert in machine-learning and AI, compared how effective recommendations were from AI-guided tools/models that knew the patient’s race and factors uniquely relevant to African American patients against tools/models that didn’t. The team found that recommendations based on “race-blind” AI models—those that do not know the patient’s race—tended to recommend medications that were less effective for African American patients.

“Antidepressant recommendations from race-specific models outperformed recommendations from general models across all antidepressants studied. The findings highlight why clinical AI, like clinical practice, shouldn’t rely solely on general-population patterns when prescribing for African Americans with depression,” said Vladimir Cardenas, master of science in health informatics.

The risk of inaccuracy

“If AI systems are not trained on correct information, including patient demographic information, such as race, it will give incorrect or inaccurate information, which can result in people ending up with less effective medications,” said Alemi.

Alemi and his co-researchers observed that when advising patients on options for treating depression. “AI systems could be biased against African Americans, recommending antidepressants that work for general, mostly white patients, but not for African Americans,” said Alemi.

Race itself is not a biological risk

Researchers looked at bias in an AI system meant to guide treatment for major depressive disorder (MDD)—and whether race-blind models miss important signals for African American patients. The AI system used medical history—including whether a patient completed the full dose of the antidepressant—to recommend a medication. Researchers coded whether a patient discontinued the use of the antidepressant as a measure of AI-guided treatment failure or success.

The study underscores that race is not a biological determinant of depression or treatment response, emphasizing the social and environmental factors that affect depression. Some of the more common factors among African American patients may be poverty, low education, exposure to violence, discrimination, cultural stigma and negative attitudes toward mental health, and low access to mental health treatment resources.

“These data highlight the need to tailor antidepressants to fit the patient’s individual medical history. Clinicians do this, and if done right, an AI system can help clinicians do so as well,” said Cardenas.

“I hope that our approach will help inform AI in health care design and governance. This way we can truly pursue AI that improves the health of all,” said Cardenas.

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