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

Digital Health Reviews (1862) Depression and Mood Disorders; Suicide Prevention (1790) Machine Learning (2285) Artificial Intelligence (2432) Perinatal Depression; Postpartum Depression; PPD (144) Computer-Aided Diagnosis (283)

Published on in Vol 28 (2026)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/77376, first published .
AI for Detecting and Predicting Postpartum Depression: Scoping Review

AI for Detecting and Predicting Postpartum Depression: Scoping Review

AI for Detecting and Predicting Postpartum Depression: Scoping Review

Authors of this article:

Mais Alkhateeb1, 2, 3 Author Orcid Image ;   Ajisha Nayeem2 Author Orcid Image ;   Arfan Ahmed2 Author Orcid Image ;   Mohammed Alsahli4 Author Orcid Image ;   Javaid Sheikh2 Author Orcid Image ;   Alaa Abd-Alrazaq2 Author Orcid Image
Article Authors Cited by (3) Tweetations (3) Metrics

Journals

  1. Dinescu R, Motofelea A, Luminosu P, Loichita M, Motofelea N, Sas I. Risk-Stratified Screening for Perinatal Depression and Anxiety: Integrating Sexual Function, Self-Esteem, and Psychosocial Context. Diagnostics 2026;16(3):412 View
  2. Zhu Y, Wang C, Chen X, Zhao Y, Yang L, Wang Q, Lv J, Wang W. Effects of hydromorphone for patient-controlled intravenous analgesia on postpartum depression and sleep disorders after cesarean section: a randomized, double-blind controlled trial. Frontiers in Pharmacology 2026;17 View
  3. Das J, Prima M, Rimu F, Hossain P, Das T, Nesa F, Rahman F, Islam A, Mazumder H, Tasnim S, Hossain M. Mental Health and Wellbeing of Rohingya Refugees: A Scoping Review. Journal of Racial and Ethnic Health Disparities 2026 View