{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,4]],"date-time":"2026-08-04T10:44:28Z","timestamp":1785840268298,"version":"3.56.0"},"reference-count":34,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2026,8,4]],"date-time":"2026-08-04T00:00:00Z","timestamp":1785801600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Clin. Med.","JCM"],"abstract":"<jats:p>Background: Artificial intelligence (AI), which spans machine learning (ML), deep learning (DL), computer vision, and natural language processing (NLP), is now used across obstetric care, including ultrasound interpretation (biometry, anomaly detection), fetal monitoring (cardiotocography), maternal risk stratification (preeclampsia, preterm birth, hemorrhage), labor and delivery decision support, genomic screening, and telehealth. Methods: We conducted a narrative (non-systematic) review of the literature published between 2016 and 2026, distinguishing the level of evidence supporting each application. Results: Reported performance is frequently high for image-based tasks such as fetal biometry and anomaly detection (accuracy and AUC often exceeding 0.85), whereas intrapartum CTG analysis remains modest (AUROC ~0.60\u20130.70, overlapping the inter-observer variability of clinicians). Most published evidence is retrospective and internally validated; comparatively few tools have undergone external or prospective validation, and only a small number have received regulatory clearance. Limitations: The evidence base is heterogeneous, external validation and calibration are often absent, and we did not perform a formal risk-of-bias appraisal. Conclusions: AI has real potential to improve prenatal diagnosis and individualized care, but claims that it is ready for the clinic are often premature. Prospective and external validation, calibration and clinical-utility assessment, transparent reporting, attention to bias and equity, and sustained clinician oversight are prerequisites for safe adoption.<\/jats:p>","DOI":"10.3390\/jcm15156059","type":"journal-article","created":{"date-parts":[[2026,8,4]],"date-time":"2026-08-04T09:15:22Z","timestamp":1785834922000},"page":"6059","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Artificial Intelligence in Obstetrics: Current Trends and Future Directions"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-9549-3013","authenticated-orcid":false,"given":"Ittai","family":"Many","sequence":"first","affiliation":[{"name":"Gray Faculty of Medical and Health Sciences, Tel Aviv University, Tel Aviv 6997801, Israel"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ariel","family":"Many","sequence":"additional","affiliation":[{"name":"Gray Faculty of Medical and Health Sciences, Tel Aviv University, Tel Aviv 6997801, Israel"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,8,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1038\/s41591-018-0300-7","article-title":"High-performance medicine: The convergence of human and artificial intelligence","volume":"25","author":"Topol","year":"2019","journal-title":"Nat. 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