Artificial Intelligence-Augmented Clinical Decision Support Systems for Pregnancy Care: Systematic Review.
Xinnian LinChen LiangJihong LiuTianchu LyuNadia GhummanBerry CampbellPublished in: Journal of medical Internet research (2024)
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.
Keyphrases
- artificial intelligence
- systematic review
- clinical decision support
- healthcare
- big data
- public health
- electronic health record
- case control
- machine learning
- pregnant women
- pregnancy outcomes
- current status
- preterm birth
- magnetic resonance imaging
- deep learning
- quality improvement
- meta analyses
- metabolic syndrome
- pain management
- adipose tissue
- type diabetes
- body mass index
- birth weight
- insulin resistance
- physical activity
- climate change