Ensuring generalizability and clinical utility in mental health care applications: Robust artificial intelligence-based treatment predictions in diverse psychosis populations.

ABSTRACT:

Aim

Artificial Intelligence (AI)-based prediction models of treatment response promise to revolutionize psychiatric care by enabling personalized treatment, but very few have been thoroughly tested in different samples or compared to current clinical standards. Here we present models predicting antipsychotic response and assess their clinical utility in a robust methodological framework.

Methods

Machine learning models were trained and cross-validated on clinical and sociodemographic data from 594 individuals with established schizophrenia (NCT00014001) and 323 individuals with first episode psychosis (NCT03510325). Models predicted four measures of antipsychotic response at 3 months after baseline. Clinical utility was assessed using decision curve and calibration curve ana

SUBMITTER: Coutts F 

PROVIDER: S-EPMC12757767 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature