JMIR Publications

ABSTRACT

Background:

Despite emerging application of clinical decision support systems (CDSS) in pregnancy care and the proliferation of artificial intelligence (AI) over the last decade, it remains understudied regarding the role of AI in CDSS specialized for pregnancy care.

Objective:

To identify and synthesize AI augmented CDSS on pregnancy care, CDSS functionality, AI methodologies, and clinical implementation, we reported a narrative review based on empirical studies that examined AI augmented CDSS in pregnancy care

Methods:

We retrieved studies that examined AI augmented CDSS in pregnancy care using database queries involved with titles, abstracts, keywords, and MeSH terms. Bibliographic records from their inception to 2022 were retrieved from PubMed/MEDLINE (n=206), EMBASE (n=101), and ACM Digital Library (n=377), followed by eligibility screening and literature review. The eligibility criteria include empirical studies that 1) developed and/or tested AI methods, 2) developed and/or tested CDSS or CDSS components, and 3) focused on pregnancy care. Data of studies used for review and appraisal include title, abstract, keywords, MeSH terms, full text, and supplements. Publications with ancillary information and/or overlapping outcomes were synthesized as one single study. Reviewers independently reviewed and assessed the quality of selected studies.

Results:

We identified 30 distinct studies out of 684 studies from their inception to 2022. Topics of clinical applications covered AI augmented CDSS from prenatal, early pregnancy, obstetric care, and postpartum care. Topics of CDSS functions include diagnostic support, clinical prediction, therapeutics recommendation, and knowledge base.

Conclusions:

Our review acknowledged recent advances in CDSS studies including early diagnosis of prenatal abnormalities, cost effective surveillance, prenatal ultrasound support, and ontology development. To recommend for future directions, we also noted key gaps from existing studies, including 1) decision support in current childbirth deliveries without using observational data from consequential fetal/maternal outcomes in future pregnancies; 2) scarcity of studies in identifying several high-profile biases from CDSS, including social determinants of health (SDOH) highlighted by the American College of Obstetricians and Gynecologists; and 3) chasm between internally validated CDSS models, external validity, and clinical implementation. Clinical Trial: This study is registered with International Prospective Register of Systematic Reviews (PROSPERO) on 09/05/2023. The registration number is #460907.