  Precision psychiatry is an ermerging field that aims to provide
individualized approaches to mental health care. Multivariate analysis and
machine learning are used to create outcome prediction models based on clinical
data such as demographics, symptom assessments, genetic information, and brain
imaging. While much emphasis has been placed on technical innovation, the
complex and varied nature of mental health presents significant challenges to
the successful implementation of these models. From this perspective, I review
ten challenges in the field of precision psychiatry, including the need for
studies on real-world populations and realistic clinical outcome definitions,
consideration of treatment-related factors such as placebo effects and
non-adherence to prescriptions. Fairness, prospective validation in comparison
to current practice and implementation studies of prediction models are other
key issues that are currently understudied. A shift is proposed from
retrospective studies based on linear and static concepts of disease towards
prospective research that considers the importance of contextual factors and
the dynamic and complex nature of mental health.

Quantitative Methods;Machine Learning