{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,24]],"date-time":"2026-04-24T15:42:42Z","timestamp":1777045362617,"version":"3.51.4"},"reference-count":124,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T00:00:00Z","timestamp":1761091200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Ministry of Economic Development of the Russia","award":["000000C313925P4G0002"],"award-info":[{"award-number":["000000C313925P4G0002"]}]},{"name":"Ivannikov Institute for System Programming of the Russian Academy of Sciences","award":["139-15-2025-011"],"award-info":[{"award-number":["139-15-2025-011"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JCM"],"abstract":"<jats:p>Background\/Objectives: Accurate diagnosis, prognosis, and prediction of treatment response are essential in managing gynecologic cancers and maintaining patient quality of life. Computational pathology, powered by artificial intelligence (AI), offers a transformative opportunity for objective histopathological assessment. This review provides a comprehensive, user-oriented overview of existing AI tools for the characterization of gynecological cancers, critically evaluating their clinical applicability and identifying key challenges for future development. Methods: A systematic literature search was conducted in PubMed and Web of Science for studies published up to 2025. The search focused on AI tools developed for the diagnosis, prognosis, or treatment prediction of gynecologic cancers based on histopathological images. After applying selection criteria, 36 studies were included for in-depth analysis, covering ovarian, uterine, cervical, and other gynecological cancers. Studies on cytopathology and pure tumor detection were excluded. Results: Our analysis identified AI tools addressing critical clinical tasks, including histopathologic subtyping, grading, staging, molecular subtyping, and prediction of therapy response (e.g., to platinum-based chemotherapy or PARP inhibitors). The performance of these tools varied significantly. While some demonstrated high accuracy and promising results in internal validation, many were limited by a lack of external validation, potential biases from training data, and performance that is not yet sufficient for routine clinical use. Direct comparison between studies was often hindered by the use of non-standardized evaluation metrics and evolving disease classifications over the past decade. Conclusions: AI tools for gynecologic cancers represent a promising field with the potential to significantly support pathological practice. However, their current development is heterogeneous, and many tools lack the robustness and validation required for clinical integration. There is a pressing need to invest in the creation of clinically driven, interpretable, and accurate AI tools that are rigorously validated on large, multicenter cohorts. Future efforts should focus on standardizing evaluation metrics and addressing unmet diagnostic needs, such as the molecular subtyping of rare tumors, to ensure these technologies can reliably benefit patient care.<\/jats:p>","DOI":"10.3390\/jcm14217465","type":"journal-article","created":{"date-parts":[[2025,10,23]],"date-time":"2025-10-23T05:20:44Z","timestamp":1761196844000},"page":"7465","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Artificial Intelligence Tools for Supporting Histopathologic and Molecular Characterization of Gynecological Cancers: A Review"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8739-5209","authenticated-orcid":false,"given":"Aleksandra","family":"Asaturova","sequence":"first","affiliation":[{"name":"1st Pathology Department, FSBI \u201cNational Medical Research Centre for Obstetrics, Gynecology and Perinatology Named After Academician V.I.Kulakov\u201d, The Ministry of Health of the Russian Federation, Akademika Oparina Street, 4, 117198 Moscow, Russia"},{"name":"Pathology and Clinical Pathology Department, Institute of Human Biology and Pathology, Pirogov Russian National Research Medical University, Ostrovityanova Street, 1, 117279 Moscow, Russia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1993-1705","authenticated-orcid":false,"given":"Jo\u00e3o","family":"Pinto","sequence":"additional","affiliation":[{"name":"Pathology Laboratory, Institute of Molecular Pathology and Immunology of University of Porto (IPATIMUP), Rua J\u00falio Amaral de Carvalho 45, 4200-135 Porto, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8312-1681","authenticated-orcid":false,"given":"Ant\u00f3nio","family":"Polonia","sequence":"additional","affiliation":[{"name":"Pathology Laboratory, Institute of Molecular Pathology and Immunology of University of Porto (IPATIMUP), Rua J\u00falio Amaral de Carvalho 45, 4200-135 Porto, Portugal"},{"name":"Escola de Medicina e Ci\u00eancias Biom\u00e9dicas, Universidade Fernando Pessoa, Pra\u00e7a de 9 de Abril 349, 4249-004 Porto, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6771-2163","authenticated-orcid":false,"given":"Evgeny","family":"Karpulevich","sequence":"additional","affiliation":[{"name":"Ivannikov Institute for System Programming of the Russian Academy of Science, Research Center for Trusted Artificial Intelligence, 109004 Moscow, Russia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7201-6605","authenticated-orcid":false,"given":"Xavier","family":"Mattias-Guiu","sequence":"additional","affiliation":[{"name":"Department of Pathology, Hospital U Arnau de Vilanova & University of Lleida, Institut de Recerca Biom\u00e8dica de Lleida, 28029 Barcelona, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7642-1280","authenticated-orcid":false,"given":"Catarina","family":"Eloy","sequence":"additional","affiliation":[{"name":"Pathology Laboratory, Institute of Molecular Pathology and Immunology of University of Porto (IPATIMUP), Rua J\u00falio Amaral de Carvalho 45, 4200-135 Porto, Portugal"},{"name":"Pathology Department, Medical Faculty of University of Porto, Alameda Prof. Hern\u00e2ni Monteiro, 4200-319 Porto, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2025,10,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"iv72","DOI":"10.1093\/annonc\/mdx220","article-title":"Cervical cancer: ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up","volume":"28","author":"Marth","year":"2017","journal-title":"Ann. 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