{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,19]],"date-time":"2026-06-19T13:53:20Z","timestamp":1781877200140,"version":"3.54.5"},"reference-count":24,"publisher":"Wiley","license":[{"start":{"date-parts":[[2026,6,19]],"date-time":"2026-06-19T00:00:00Z","timestamp":1781827200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,6,19]],"date-time":"2026-06-19T00:00:00Z","timestamp":1781827200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["obgyn.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Intl J Gynecology &amp;amp; Obste"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Artificial intelligence (AI) is increasingly being investigated as a decision\u2010support tool in controlled ovarian stimulation (COS) during in\u00a0vitro fertilization. Machine learning and deep learning algorithms have demonstrated promising predictive performance for outcomes including ovarian response, mature oocyte yield, premature luteinizing hormone rise, gonadotropin dose adjustment, and optimization of trigger timing. Convolutional neural networks applied to three\u2010dimensional ultrasound imaging have also enabled automated follicular monitoring and more standardized assessment of follicular development. This narrative review summarizes current AI applications in COS and critically evaluates the methodological quality, validation status, and clinical relevance of available models. Most published studies are retrospective and based on single\u2010center data sets, with limited external validation and scarce prospective randomized evidence. Furthermore, many reported improvements relate primarily to surrogate laboratory outcomes rather than clinically meaningful endpoints such as live birth rates. Although AI\u2010assisted COS demonstrates considerable potential for individualized treatment and workflow optimization, important barriers remain, including model interpretability, generalizability, ethical considerations, data privacy, and regulatory oversight. Future progress will require multicenter collaborations, prospective clinical validation, explainable AI frameworks, and careful integration of AI tools into clinician\u2010guided reproductive care.<\/jats:p>","DOI":"10.1002\/ijgo.71163","type":"journal-article","created":{"date-parts":[[2026,6,19]],"date-time":"2026-06-19T13:11:23Z","timestamp":1781874683000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Artificial intelligence\u2013assisted controlled ovarian stimulation in in\u00a0vitro fertilization: A critical narrative review"],"prefix":"10.1002","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-2772-3684","authenticated-orcid":false,"given":"E.","family":"Deligeoroglou","sequence":"first","affiliation":[{"name":"Medical School, 1st Department of Obstetrics and Gynecology, \u201cPapageorgiou\u201d General Hospital Aristotle University of Thessaloniki  Thessaloniki Greece"},{"name":"HYGEIA IVF Embryogenesis  Athens Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"G.","family":"Makrakis","sequence":"additional","affiliation":[{"name":"Royal Surrey Country Hospital Royal Surrey NHS Foundation Trust  Guildford UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"E.","family":"Makrakis","sequence":"additional","affiliation":[{"name":"HYGEIA IVF Embryogenesis  Athens Greece"},{"name":"Medical School University of Nicosia  Nicosia Cyprus"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,6,19]]},"reference":[{"issue":"7","key":"e_1_2_12_2_1","doi-asserted-by":"crossref","first-page":"R231","DOI":"10.1016\/j.cub.2019.02.034","article-title":"Neural network models and deep learning","volume":"29","author":"Kriegeskorte N","year":"2019","journal-title":"Curr 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