Accessibility settings

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

Artificial Intelligence (2377) Human Factors and Usability Case Studies (1076) Electronic Health Records (1386) Depression and Mood Disorders; Suicide Prevention (1769) Machine Learning (2252) Intentional Self-Harm (147) Pediatrics (175) Mental Health Issues in Adolescence (681) Ethical, Legal, and Social Issues in AI (124)

Published on in Vol 27 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/57602, first published .
Exploring the Perspectives of Pediatric Health Care Providers, Youth Patients, and Caregivers on Machine Learning Suicide Risk Classification: Mixed Methods Study

Exploring the Perspectives of Pediatric Health Care Providers, Youth Patients, and Caregivers on Machine Learning Suicide Risk Classification: Mixed Methods Study

Exploring the Perspectives of Pediatric Health Care Providers, Youth Patients, and Caregivers on Machine Learning Suicide Risk Classification: Mixed Methods Study

Authors of this article:

Rohan R Dayal1 Author Orcid Image ;   Pua Lani Yang1 Author Orcid Image ;   Laura Nicole Sisson2 Author Orcid Image ;   Mira Bajaj3 Author Orcid Image ;   Shannon Archuleta1 Author Orcid Image ;   Sophie Yao4 Author Orcid Image ;   Daniel H Park4 Author Orcid Image ;   Hanae Fujii-Rios5 Author Orcid Image ;   Emily E Haroz6 Author Orcid Image
Article Authors Cited by (2) Tweetations Metrics

Journals

  1. Cox J, Ivory B, Salmon P, Read G. Investigating acceptance of current and future artificial intelligence systems for suicide prevention. AI & SOCIETY 2026 View
  2. Woo Y, Jung K. Artificial Intelligence–Driven Tools in Mental Health Service Delivery: A Scoping Review. Healthcare 2026;14(7):943 View