Modern views of machine learning for precision psychiatry

In light of the National Institute of Mental Health (NIMH)’s Research Domain Criteria (RDoC), the advent of functional neuroimaging, novel technologies and methods provide new opportunities to develop precise and personalized prognosis and diagnosis of mental disorders. Machine learning (ML) and artificial intelligence (AI) technologies are playing an increasingly critical role in the new era of precision psychiatry. Combining ML/AI with neuromodulation technologies can potentially provide explainable solutions in clinical practice and effective therapeutic treatment. Advanced wearable and mobile technologies also call for the new role of ML/AI for digital phenotyping in mobile mental health. In this review, we provide a comprehensive review of ML methodologies and applications by combining neuroimaging, neuromodulation, and advanced mobile technologies in psychiatry practice. We further review the role of ML in molecular phenotyping and cross-species biomarker identification in precision psychiatry. We also discuss explainable AI (XAI) and neuromodulation in a closed human-in-the-loop manner and highlight the ML potential in multi-media information extraction and multi-modal data fusion. Finally, we discuss conceptual and practical challenges in precision psychiatry and highlight ML opportunities in future research. Mental health issues are an epidemic in the United States and the world and have imposed a tremendous burden to the healthcare system and society. To date, there is still a lack of biomarkers and individualized treatment guidelines for mental illnesses. In recent years, machine learning (ML) and artificial intelligence (AI) have become increasingly popular in analyzing complex patterns of neural and behavioral data for psychiatry. We provide a comprehensive review of ML methodologies and applications in precision psychiatry. We argue that advances in ML-powered modern technologies will create a paradigm shift in the current practice in diagnosis, prognosis, monitoring, and treatment of mental illnesses. We discuss conceptual and practical challenges in precision psychiatry and highlight future research opportunities in ML. Managing and analyzing a large amount of neuroimaging and behavioral data related to mental health have become increasingly important in both precision psychiatry and data science research. Chen et al. present a comprehensive review of machine-learning methodologies, artificial intelligence (AI)-powered technologies and appl