Artificial Intelligence for Mental Health and Mental Illnesses: An Overview

Purpose of review: Artificial intelligence (AI) holds promise to transform mental healthcare yet carries potential pitfalls. This article provides an overview of AI and current applications in healthcare, a review of recent original research on AI specific to mental health, and a discussion of how AI can supplement clinical practice while considering limitations, areas needing additional research, and ethical implications. Recent findings: Twenty-eight studies using electronic health records (EHRs), mood rating scales, brain imaging data, novel monitoring systems (e.g., smartphone, video), and social media platforms were reviewed to predict, classify, or subgroup mental illnesses including depression, schizophrenia/other psychiatric illnesses, and suicidal ideation/attempts. Studies showed high accuracies and demonstrated AI’s potential, but most represent early proof-of-concept identifying feasible machine learning (ML) approaches. Summary: As AI techniques are refined, they may help redefine mental illnesses more objectively than DSM-5, enable earlier/prodromal detection, and personalize treatments. Caution is warranted to avoid over-interpreting preliminary results, and further work is needed to bridge AI in mental health research and clinical care.