New developments in machine learning (ML) and artificial intelligence (AI) hold great promise to revolutionize mental health care. In this context, ML and AI have been deployed for several different goals, including 1) the early detection of mental disorders, 2) the optimization of personalized treatments based on the individual characteristics of patients, 3) the better characterization of disorders detrimental to mental well-being and quality of life, as well as a better description of projected trajectories over time, and 4) the development of new treatments for mental health care. Despite their great potential to transform mental health care and occasional breakthroughs, ML and AI have not yet fully achieved these goals.This research topic aims to bridge the gap between the potential uses of ML and AI and their practical application in standard mental health care. More specifically, we welcome original research submissions applying ML and AI to promote public health by reducing the burden of chronic disorders with detrimental effects on well-being (e.g., psychopathological distress), and improving quality of life. We also welcome submissions applying ML and AI in heterogeneous datasets (e.g., subjective scales and questionnaires, biomarkers, (neuro)psychological assessments, etc.) from Big Data sources (e.g., large datasets of clinical populations, electronic health records from nationally representative cohorts, and/or biobanks, studies using experiencing sampling methods, etc.) to gain mechanistic insight on how different chronic conditions associated with psychopathological distress can affect patient well-being and quality of life. Finally, we also welcome opinion papers and reviews on how to develop AI applications in mental health care responsibly, while integrating biopsychosocial aspects of patients to promote better mental health care. We are looking forward to the following forms of submission: Original Research Articles, Review Articles, Clinical Trials, Case Reports, Mini Review Articles, General Commentaries, Perspectives, Hypotheses & Theories.
New developments in machine learning (ML) and artificial intelligence (AI) hold great promise to revolutionize mental health care. In this context, ML and AI have been deployed for several different goals, including 1) the early detection of mental disorders, 2) the optimization of personalized treatments based on the individual characteristics of patients, 3) the better characterization of disorders detrimental to mental well-being and quality of life, as well as a better description of projected trajectories over time, and 4) the development of new treatments for mental health care. Despite their great potential to transform mental health care and occasional breakthroughs, ML and AI have not yet fully achieved these goals.This research topic aims to bridge the gap between the potential uses of ML and AI and their practical application in standard mental health care. More specifically, we welcome original research submissions applying ML and AI to promote public health by reducing the burden of chronic disorders with detrimental effects on well-being (e.g., psychopathological distress), and improving quality of life. We also welcome submissions applying ML and AI in heterogeneous datasets (e.g., subjective scales and questionnaires, biomarkers, (neuro)psychological assessments, etc.) from Big Data sources (e.g., large datasets of clinical populations, electronic health records from nationally representative cohorts, and/or biobanks, studies using experiencing sampling methods, etc.) to gain mechanistic insight on how different chronic conditions associated with psychopathological distress can affect patient well-being and quality of life. Finally, we also welcome opinion papers and reviews on how to develop AI applications in mental health care responsibly, while integrating biopsychosocial aspects of patients to promote better mental health care. We are looking forward to the following forms of submission: Original Research Articles, Review Articles, Clinical Trials, Case Reports, Mini Review Articles, General Commentaries, Perspectives, Hypotheses & Theories.