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This paper is in the following e-collection/theme issue:

Foundation Models and Their Applications in AI (57) Digital Mental Health Interventions, e-Mental Health and Cyberpsychology (2267) Foundations of AI (66) Applications of AI (345) Machine Learning (2235) Depression and Mood Disorders; Suicide Prevention (1759) Clinical Information and Decision Making (2073) Voice Phenotyping and Vocal Biomarkers (22)

Published on in Vol 4 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/69149, first published .
Digital Phenotyping for Detecting Depression Severity in a Large Payor-Provider System: Retrospective Study of Speech and Language Model Performance

Digital Phenotyping for Detecting Depression Severity in a Large Payor-Provider System: Retrospective Study of Speech and Language Model Performance

Digital Phenotyping for Detecting Depression Severity in a Large Payor-Provider System: Retrospective Study of Speech and Language Model Performance

Authors of this article:

Bradley Karlin1, 2 Author Orcid Image ;   Doug Henry1 Author Orcid Image ;   Ryan Anderson1 Author Orcid Image ;   Salvatore Cieri1 Author Orcid Image ;   Michael Aratow3 Author Orcid Image ;   Elizabeth Shriberg3 Author Orcid Image ;   Michelle Hoy3 Author Orcid Image
Article Authors Cited by (2) Tweetations Metrics Author Video

Journals

  1. Acquati C, Aratow M, Nazreen T, Bhattacharjee A, Marra I, Alexander A. Evaluating an AI-Enabled Mobile Mental Health Monitoring Tool Among Family Caregivers of Adults Living With Cancer: Single-Arm Feasibility and Acceptability Trial Protocol. JMIR Research Protocols 2026;15:e83276 View
  2. Verhees F, Wiest I, Kather J, Kambeitz J, Mikolas P. Blocking the Reflection: Milestones and Hurdles for Digital Twins in Mental Health. Pharmacopsychiatry 2026 View