Artificial intelligence for schizophrenia: from unimodal prediction to multimodal characterization

Current literature highlights a shift from unimodal prediction to holistic, multimodal characterization of schizophrenia. Transforming these artificial intelligence models into clinical tools, however, requires careful attention to patient privacy and data bias, alongside rigorous validation across diverse populations and settings.

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Publication Details

Journal
Current Opinion in Psychiatry
Published
2026-01-19
DOI
https://doi.org/10.1097/yco.0000000000001076
Primary Topic
Machine Learning in Healthcare
Type
article
Field-Weighted Citation Impact
0.00
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article

Artificial intelligence for schizophrenia: from unimodal prediction to multimodal characterization

Xinhui Li, Vince D Calhoun
Current Opinion in Psychiatry
Machine Learning in Healthcare
article

Artificial intelligence for schizophrenia: from unimodal prediction to multimodal characterization

Xinhui Li, Vince D Calhoun
article en

Abstract

Current literature highlights a shift from unimodal prediction to holistic, multimodal characterization of schizophrenia. Transforming these artificial intelligence models into clinical tools, however, requires careful attention to patient privacy and data bias, alongside rigorous validation across diverse populations and settings.

Current Opinion in PsychiatryVol. 39(3)
Georgia Institute of Technology (US), Emory University (US), Georgia State University (US), Center for Translational Research in Neuroimaging and Data Science (US)
Peace, Justice and strong institutions
Openalex Percentile: Top 64%
Machine Learning in Healthcare
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