Document Type
Journal Article
Publication Title
Computers in Biology and Medicine
Volume
177
PubMed ID
38838557
Publisher
Elsevier
School
School of Nursing and Midwifery
RAS ID
71463
Funders
Murdoch University
Grant Number
ID IC2023-GAIA/18
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.
DOI
10.1016/j.compbiomed.2024.108685
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Comments
Kwok, W. H., Zhang, Y., & Wang, G. (2024). Artificial intelligence in perinatal mental health research: A scoping review. Computers in Biology and Medicine, 108685. https://doi.org/10.1016/j.compbiomed.2024.108685