Introduction

Abstract

Artificial intelligence (AI) has the potential to transform the delivery and management of health care and improve biomedical research. Brain and mental health could significantly benefit from this technological transformation. Some of the most promising applications of AI in brain and mental health include the use of deep learning algorithms for early detection and diagnosis, as well as automated learning and the infusion of AI capabilities in everyday technologies such as smartphones, assistive social robots, and intelligent assistive technologies for continuous health monitoring and screening (e.g., Alzheimer’s disease and schizophrenia) or for the assistance of psychogeriatric and neurorehabilitation patients. In addition, machine learning (ML) can also be used to improve existing neuropsychiatric therapies and allow new indications for existing drugs and tailor them to the individual patient through precision medicine approaches. For example, Watson, an AI-driven question-answering computing system developed by IBM, has proven to make similar treatment recommendations as human experts in 99% of the cases, and in 30% of the cases, Watson found treatment options missed by human physicians [1]. In addition, Watson can perform tasks such as data integration and aggregation, assessment of patients’ risk to develop a particular disease or to require high cost treatment [2].