Published on in Vol 9 (2025)
Preprints (earlier versions) of this paper are
available at
https://preprints.jmir.org/preprint/56057, first published
.

Journals
- Wang L, Wang C, Li C, Murai T, Bai Y, Song Z, Zhang S, Zhang Q, Huang Y, Bi X, Jiang J. AI-assisted multi-modal information for the screening of depression: a systematic review and meta-analysis. npj Digital Medicine 2025;8(1) View
- Feng L, Li N, Jilka S, Firth J, Wang G. Understanding mood through facial expressions: opportunities and challenges in depression detection. Science Bulletin 2025;70(22):3687 View
- Li H, Li S. Computer‐Assisted Performance‐Based Assessment for Mental Health: A Scoping Review. PsyCh Journal 2026;15(2) View
- Okesanya O, Oso T, Hassan M, Adebayo U, Vandy A, Othman Z, Ali I, Ahmed M, Musa S, Eshun G, Lucero-Prisno III D. Applications of artificial intelligence and machine learning in the diagnosis and management of major depressive disorder: a systematic review. The Egyptian Journal of Neurology, Psychiatry and Neurosurgery 2026;62(1) View
- Crema C, De Francesco S, Baronio C, Boccali A, Demaria C, Tura G, Archetti D, Redolfi A. Multimodal AI-based systems in major depressive disorder: a review of clinical and translational applications. Frontiers in Digital Health 2026;8 View
Conference Proceedings
- Anuj M, Nancy R G. 2025 9th International Conference on Electronics, Communication and Aerospace Technology (ICECA). A Multi-Modal AI Framework for Real-Time Depression and Anxiety Detection using Voice, Text, and Facial Cues View
