Machine Learning for Mental Health: Applications, Challenges, and the Clinician's Role

Purpose of review: This review aims to evaluate the current psychiatric applications and limitations of machine learning (ML), defined as techniques used to train algorithms to improve performance at a task based on data. The review emphasizes the clinician's role in ensuring equitable and effective patient care and seeks to inform mental health providers about the importance of clinician involvement in these technologies.

Recent findings: ML in psychiatry has advanced through electronic health record integration, disease phenotyping, and remote monitoring through mobile applications. However, these applications face challenges related to health equity, privacy, translation to practice, and validation. Clinicians play crucial roles in ensuring data quality, mitigating biases, promoting algorithm transparency, guiding clinical implementation, and advocating for ethical and patient-centered use of ML tools. Clinicians are essential in addressing the challenges of ML, ensuring its ethical application, and promoting equitable care, thus improving the effectiveness of ML in practice.