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

Journal: Current psychiatry reports
PMID: 39523249

Abstract

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.

Authors

  • Sorabh Singhal
    Department of Psychiatry, University of Colorado School of Medicine, 1890 N Revere Ct, F546 AHSB, Suite 4100, Rm 4102, Aurora, CO, USA. Sorabh.Singhal@cuanschutz.edu.
  • Danielle L Cooke
    Department of Psychiatry, University of Colorado School of Medicine, 1890 N Revere Ct, F546 AHSB, Suite 4100, Rm 4102, Aurora, CO, USA.
  • Ricardo I Villareal
    Department of Psychiatry, University of Colorado School of Medicine, 1890 N Revere Ct, F546 AHSB, Suite 4100, Rm 4102, Aurora, CO, USA.
  • Joel J Stoddard
    Department of Psychiatry, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA.
  • Chen-Tan Lin
    Department of Medicine, University of Colorado School of Medicine, Aurora, CO, USA.
  • Allison G Dempsey
    Department of Psychiatry, University of Colorado School of Medicine, 1890 N Revere Ct, F546 AHSB, Suite 4100, Rm 4102, Aurora, CO, USA.

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