TY  - JOUR
AU  - Lin, Xinnian
AU  - Liang, Chen
AU  - Liu, Jihong
AU  - Lyu, Tianchu
AU  - Ghumman, Nadia
AU  - Campbell, Berry
PY  - 2024
DA  - 2024/9/16
TI  - Artificial Intelligence–Augmented Clinical Decision Support Systems for Pregnancy Care: Systematic Review
JO  - J Med Internet Res
SP  - e54737
VL  - 26
KW  - artificial intelligence
KW  - biomedical ontologies
KW  - clinical decision support systems
KW  - implementation science
KW  - obstetrics
KW  - pregnancy
KW  - AI
KW  - systematic review
KW  - CDSS
KW  - functionality
KW  - methodology
KW  - implementation
KW  - database query
KW  - database queries
KW  - bibliography
KW  - record
KW  - records
KW  - eligibility
KW  - literature review
KW  - prenatal
KW  - early pregnancy
KW  - obstetric care
KW  - postpartum care
KW  - pregnancy care
KW  - diagnostic support
KW  - clinical prediction
KW  - knowledge base
KW  - therapeutic
KW  - therapeutics
KW  - recommendation
KW  - recommendations
KW  - diagnosis
KW  - abnormality
KW  - abnormalities
KW  - cost-effective
KW  - surveillance
KW  - ultrasound
KW  - ontology
AB  - Background: Despite the 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 in pregnancy care, CDSS functionality, AI methodologies, and clinical implementation, we reported a systematic 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 (Medical Subject Headings) 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 or tested AI methods, (2) developed 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 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 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 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 or maternal outcomes in future pregnancies; (2) scarcity of studies in identifying several high-profile biases from CDSS, including social determinants of health highlighted by the American College of Obstetricians and Gynecologists; and (3) chasm between internally validated CDSS models, external validity, and clinical implementation. 
SN  - 1438-8871
UR  - https://www.jmir.org/2024/1/e54737
UR  - https://doi.org/10.2196/54737
DO  - 10.2196/54737
ID  - info:doi/10.2196/54737
ER  -