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Generative AI for precision neuroimaging biomarker development in psychiatry
Susan N Wright1, Alan Anticevic2
1Division of Neuroscience and Behavior, National Institute on Drug Abuse, National Institutes of Health, 11601 Landsdown St., Three White Flint North (3WFN), MSC 6018, Rockville, MD 20852, United States.
Psychiatry Research
|June 23, 2024
Summary
Generative AI shows potential for advancing neuroimaging biomarkers in psychiatry, but challenges like dataset size and feature selection need addressing for effective application in precision psychiatry and drug discovery.
Area of Science:
- Neuroimaging
- Psychiatry
- Artificial Intelligence
Background:
- Generative AI presents opportunities for developing neuroimaging biomarkers in psychiatry.
- Effective adoption requires addressing challenges in AI model training and feature selection.
Purpose of the Study:
- To explore generative AI's potential in quantifying brain-to-symptom associations for precision psychiatry.
- To highlight applications in drug discovery and identify challenges for advancing neuroimaging biomarkers.
Main Methods:
- Discussion of generative AI applications in neuroimaging biomarker development.
- Analysis of challenges related to dataset size and feature selection for AI models.
Main Results:
- Generative AI can potentially improve the quantification of brain-to-symptom associations.
- Identified key challenges hindering the advancement of AI-driven neuroimaging biomarkers.
Conclusions:
- Generative AI holds promise for precision psychiatry and drug discovery through enhanced neuroimaging biomarkers.
- Further research is needed to overcome current challenges for widespread adoption.
