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Artificial intelligence in perinatal mental health research: A scoping review
Journal article   Open access   Peer reviewed

Artificial intelligence in perinatal mental health research: A scoping review

Stephen Kwok, Yuanpeng Zhang and Guanjin (Brenda) Wang
Computers in Biology and Medicine, Vol.177, 108685
2024
DOI: https://doi.org/10.1016/j.compbiomed.2024.108685
Appears in  Open Access via Read & Publish Agreements
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CC BY V4.0,  Open Access

Abstract

The intersection of Artificial Intelligence (AI) and perinatal mental health research presents promising avenues, yet uncovers significant challenges for innovation. This review explicitly focuses on this multidisciplinary field and undertakes a comprehensive exploration of existing research therein. Through a scoping review guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework, we searched relevant literature spanning a decade (2013–2023) and selected fourteen studies for our analysis. We first provide an overview of the main AI techniques and their development, including traditional methods across different categories, as well as recent emerging methods in the field. Then, through our analysis of the literature, we summarize the predominant AI and ML techniques adopted and their applications in perinatal mental health studies, such as identifying risk factors, predicting perinatal mental health disorders, voice assistants, and Q&A chatbots. We also discuss existing limitations and potential challenges that hinder AI technologies from improving perinatal mental health outcomes, and suggest several promising directions for future research to meet real needs in the field and facilitate the translation of research into clinical settings.

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