AI at the Bedside of Psychiatry: Comparative Meta-Analysis of Imaging vs. Non-Imaging Models for Bipolar vs. Unipolar Depression.

ABSTRACT: Background: Differentiating bipolar disorder (BD) from unipolar major depressive disorder (MDD) at first episode is clinically consequential but challenging. Artificial intelligence/machine learning (AI/ML) may improve early diagnostic accuracy across imaging and non-imaging data sources. Methods: Following PRISMA 2020 and a pre-registered protocol on protocols.io, we searched PubMed, Scopus, Europe PMC, Semantic Scholar, OpenAlex, The Lens, medRxiv, ClinicalTrials.gov, and Web of Science (2014-8 October 2025). Eligible studies developed/evaluated supervised ML classifiers for BD vs. MDD at first episode and reported test-set discrimination. AUCs were meta-analyzed on the logit (GEN) scale using random effects (REML) with Hartung-Knapp adjustment and then back-transformed. Su

SUBMITTER: Daescu A 

PROVIDER: S-EPMC12841915 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature