Artificial Intelligence in Mental Health Care

Abstract

Artificial intelligence (AI) technology has both immense promise and possible problems in terms of transforming mental healthcare. This article provides an overview of artificial intelligence and its 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 its current limitations, areas for further research, and ethical implications regarding AI technology. To predict, classify, or subgroup mental health illnesses like depression, schizophrenia or other psychiatric illnesses, and suicidal ideation and attempts, we reviewed 28 studies of AI and mental health that used electronic health records (EHRs), mood rating scales, brain imaging data, and novel monitoring systems (e.g., smartphone, video), as well as social media platforms. Most of these studies should be viewed as early proof-of- concept works demonstrating the potential of using machine learning (ML) algorithms to address mental health questions and which types of algorithms yield the best performance. Taken as a whole, these studies revealed high accuracies and excellent examples of AI's potential in mental healthcare. It will be possible for mental health professionals to re- define mental illnesses more objectively than is currently done in the DSM-5, identify these illnesses at an earlier or prodromal stage when interventions may be more effective, and customize treatments based on a patient's particular characteristics as AI techniques continue to be developed and improved. To bridge the gap between AI in mental health research and clinical care, further effort is needed. On the other hand, caution is needed to prevent overinterpreting preliminary results.