Recommendations and future directions for supervised machine learning in psychiatry.

Area of Science:

• Psychiatry and Mental Health
• Artificial Intelligence
• Machine Learning Applications

Background:

• Machine learning (ML) methods show promise for personalized psychiatric care, including treatment tailoring and patient stratification.
• Increasing publications utilize diverse ML models and data modalities, aiming for clinical translation.
• Preliminary studies report variable accuracy, raising concerns about overestimation and methodological inconsistencies.

Purpose of the Study:

• To review current methods, recommendations, and future directions for applied machine learning in psychiatry.
• To address the need for procedural evaluation guidelines for assessing ML projects in psychiatry.
• To ensure the rigor and clinical readiness of machine learning applications in mental healthcare.

Main Methods:

• Review of current literature on machine learning applications in psychiatry.
• Analysis of best practices for model training and evaluation.
• Discussion of systematic error sources, model explainability, and clinical implementation challenges.

Main Results:

• Identified significant variability in accuracy across studies, suggesting potential overestimation and methodological issues.
• Highlighted a lack of standardized evaluation guidelines for non-expert stakeholders.
• Underscored the need for rigorous methodology and clear dissemination for clinical translation.

Conclusions:

• Applied machine learning holds transformative potential for psychiatry, contingent on methodological rigor.
• Standardized evaluation frameworks and best practices are crucial for assessing the clinical readiness of ML tools.
• Future directions should focus on addressing methodological inconsistencies, enhancing model explainability, and facilitating clinical implementation.