Modern views of machine learning for precision psychiatry

Area of Science:

• Psychiatry
• Neuroscience
• Artificial Intelligence
• Machine Learning

Background:

• The National Institute of Mental Health (NIMH) Research Domain Criteria (RDoC) framework encourages precise diagnosis and prognosis of mental disorders.
• Functional neuroimaging and novel technologies offer new avenues for personalized mental healthcare.
• Machine learning (ML) and artificial intelligence (AI) are central to the advancement of precision psychiatry.

Purpose of the Study:

• To provide a comprehensive review of ML methodologies and their applications in precision psychiatry.
• To explore the integration of neuroimaging, neuromodulation, and mobile technologies within ML frameworks.
• To discuss the role of ML in molecular phenotyping, biomarker identification, and digital phenotyping for mental health.

Main Methods:

• Review of current literature on ML/AI applications in psychiatry.
• Analysis of the synergy between ML/AI and neuroimaging, neuromodulation, and mobile health technologies.
• Exploration of explainable AI (XAI) and multi-modal data fusion techniques.

Main Results:

• ML/AI combined with neuroimaging and neuromodulation offers explainable clinical solutions and effective treatments.
• Wearable and mobile technologies enable ML/AI-driven digital phenotyping in mobile mental health.
• ML facilitates molecular phenotyping and cross-species biomarker identification in precision psychiatry.

Conclusions:

• ML/AI holds significant potential to advance precision psychiatry through data integration and advanced analytical techniques.
• Explainable AI (XAI) and closed human-in-the-loop systems are crucial for clinical translation.
• Future research should focus on addressing conceptual and practical challenges to fully leverage ML opportunities in personalized mental healthcare.