{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T16:16:36Z","timestamp":1790266596312,"version":"4.1.0"},"reference-count":90,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2026,6,18]],"date-time":"2026-06-18T00:00:00Z","timestamp":1781740800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Background\/Objectives: Hysteroscopy is the gold standard for visualization and treatment of intrauterine pathology. Because hysteroscopic interpretation remains operator-dependent, artificial intelligence (AI) has been evaluated as a tool to improve consistency, lesion recognition, and decision support. We aimed to systematically review AI, machine learning (ML), deep learning (DL), or computer-aided diagnosis (CAD) applications in hysteroscopy. Methods: A systematic search of PubMed\/MEDLINE and EBSCOhost was performed from database inception to 8 March 2026, supplemented by targeted searches. Risk of bias was assessed using QUADAS-2 (diagnostic), PROBAST (prognostic), RoB2, and structured technical quality domains. Results: Nineteen primary studies were included, covering five areas: diagnostic classification and object detection (n = 8), real-time lesion detection and localization (n = 4), segmentation and visual-field support (n = 3), operative guidance (n = 1), and prognostic or decision-support applications (n = 3). Performance was highest in narrowly defined binary tasks and in large multicenter systems (e.g., ECCADx: AUC 0.979 internal, 0.975 external) and in prognostic fertility-prediction models after hysteroscopic adhesiolysis (AUC up to 0.992). Broader multiclass classification of heterogeneous lesions showed uneven and lower performance. Most studies were single-center, retrospective, and lacked external validation. Only one randomized study linked AI support to measurable procedural outcomes. Conclusions: The available studies indicate good technical performance in selected hysteroscopic tasks, particularly binary classification, focal lesion detection, and postoperative fertility stratification. Current evidence, however, remains limited by retrospective design, operator-dependent image acquisition, inconsistent validation, and scarce outcome-based clinical testing. In the short term, the most likely role of these systems is to support image interpretation, improve visual quality control, highlight suspicious lesions, and integrate hysteroscopic findings with complementary clinical data.<\/jats:p>","DOI":"10.3390\/diagnostics16121899","type":"journal-article","created":{"date-parts":[[2026,6,19]],"date-time":"2026-06-19T09:18:43Z","timestamp":1781860723000},"page":"1899","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Artificial Intelligence, Deep Learning, and Computer Vision in Hysteroscopy: A Systematic Review"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6828-1887","authenticated-orcid":false,"given":"Rafa\u0142","family":"Watrowski","sequence":"first","affiliation":[{"name":"Department of Gynecology, Helios Hospital M\u00fcllheim, Teaching Hospital of the University of Freiburg, Heliosweg 1, 79379 M\u00fcllheim, Germany"},{"name":"Faculty of Medicine, University of Freiburg, 79106 Freiburg, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Attilio","family":"Di Spiezio Sardo","sequence":"additional","affiliation":[{"name":"Department of Public Health, School of Medicine, University of Naples Federico II, 80138 Naples, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peter","family":"T\u00f6r\u00f6k","sequence":"additional","affiliation":[{"name":"Department of Obstetrics and Gynecology, Faculty of Medicine, University of Debrecen, 4032 Debrecen, Hungary"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andrea","family":"Rosati","sequence":"additional","affiliation":[{"name":"Department of Woman\u2019s and Child Health and Public Health Sciences, Gynecologic Oncology Unit, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, 00168 Rome, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5279-3095","authenticated-orcid":false,"given":"Stoyan","family":"Kostov","sequence":"additional","affiliation":[{"name":"Research Institute, Medical University Pleven, 5800 Pleven, Bulgaria"},{"name":"Department of Gynecology, Hospital \u201cSaint Anna\u201d, 9002 Varna, Bulgaria"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ibrahim","family":"Alkatout","sequence":"additional","affiliation":[{"name":"Department of Obstetrics and Gynecology, Medical University of Innsbruck, 6020 Innsbruck, Austria"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Salvatore Giovanni","family":"Vitale","sequence":"additional","affiliation":[{"name":"Obstetrics and Gynecology Unit, \u201cGaspare Rodolico-San Marco\u201d University Hospital, Department of General Surgery and Medical Surgical Specialties, University of Catania, 95124 Catania, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,6,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"(2020). 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