Prognostic predictions in psychosis: exploring the complementary role of machine learning models

Background Predicting outcomes in schizophrenia spectrum disorders is challenging due to the variability of individual trajectories. While machine learning (ML) shows promise in outcome prediction, it has not yet been integrated into clinical practice. Understanding how ML models (MLMs) can complement psychiatrists’ predictions and bridge the gap between MLM capabilities and practical use is key. Objective This vignette study aims to compare the performance of psychiatrists and MLMs in predicting short- term symptomatic and functional remission in patients with first- episode psychosis and explore whether MLMs can improve psychiatrists’ prognostic accuracy.