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.
Authors
- Xinhui Li
- Vince D Calhoun
Institutions
- Georgia Institute of Technology (US)
- Emory University (US)
- Georgia State University (US)
- Center for Translational Research in Neuroimaging and Data Science (US)
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