Advancing Obstetric Care Through Artificial Intelligence-Enhanced Clinical Decision Support Systems: A Systematic Review

Abstract

Although artificial intelligence (AI) has grown over the past 10 years and clinical decision support systems (CDSS) have begun to be used in obstetric care, little is known about how AI functions in obstetric care-specific CDSS. We conducted a systematic review based on research studies that looked at AI-augmented CDSS in obstetric care to identify and synthesize CDSS functionality, AI techniques, clinical implementation, and AI-augmented CDSS in obstetric care. We searched four different databases (Scopus, PubMed, Web of Science, and IEEE Xplore) for relevant studies, and we found 354 studies. The studies were evaluated for eligibility based on predefined inclusion and exclusion criteria. The systematic review incorporated 30 studies after conducting an eligibility assessment of all studies. We used the Newcastle Ottawa Scale for risk bias assessment of all included studies. Medical prediction, therapeutic recommendations, diagnostic support, and knowledge dissemination constitute the key features of CDSS service offerings. The current research on CDSS included findings about early fetal anomaly detection, economical surveillance, prenatal ultrasonography assistance, and ontology development methodologies according to our study findings.

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