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This non-systematic literature review examines recent AI-augmented diagnostic approaches for endometrial cancer, emphasizing applications across histopathology, imaging, and multi-omics. 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Hacker  \nReceived: 6 May 2025  \nRevised: 23 May 2025  \nAccepted: 25 May 2025  \nPublished: 28 May 2025  \nCitation: Ansari, N.M.; Khalid, U.; Markov, D.; Bechev, K.; Aleksiev, V.; Markov, G.; Poryazova, E.  \nAI-Augmented Advancesin the Diagnostic Approaches to Endometrial Cancer. Cancers 2025, 17, 1810. [https://doi.org/10.3390/](https://doi.org/10.3390/)[ ](https://doi.org/10.3390/)cancers17111810  \nCopyright: © 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://creativecommons.org/lice](https://creativecommons.org/lice)[nses/by/4.0/](nses/by/4.0/)) .  \n1 Faculty of Medicine, Medical University of Plovdiv, 4002 Plovdiv, Bulgaria;  \n[usmankhalid957@gmail.com](usmankhalid957@gmail.com) (U.K.); [gabi_markov@abv.bg](gabi_markov@abv.bg) (G.M.)  \n2 Department of General and Clinical Pathology, Medical University of Plovdiv, 4002 Plovdiv, Bulgaria; [daniel.markov@mu-plovdiv.bg](daniel.markov@mu-plovdiv.bg) (D.M.); [kristian_bechev@abv.bg](kristian_bechev@abv.bg) (K.B.); [eporiazova@abv.bg](eporiazova@abv.bg) (E.P.)  \n3 Department of Clinical Pathology, University Multidisciplinary Hospital for Active Treatment “Pulmed”, 4002 Plovdiv, Bulgaria  \n4 Neurological Surgery, Pulmed University Hospital, 4000 Plovdiv, Bulgaria  \n5 Department of Thoracic Surgery, UMHAT “Kaspela”, 4002 Plovdiv, Bulgaria; [vl_alex@abv.bg](vl_alex@abv.bg)  \n6 Department of Cardiovascular Surgery, Medical University of Plovdiv, 4002 Plovdiv, Bulgaria  \n* [Correspondence: nabiha.m.ansari@gmail.com](Correspondence: nabiha.m.ansari@gmail.com); Tel.: +359-882354090  \nSimple Summary: Endometrial cancer is the most common cancer of the female reproductive system in developed countries. Detecting it early and accurately is important for successful treatment and better outcomes. New technologies using artificial intelligence (AI) are showing promise in helping doctors diagnose this cancer more precisely and quickly. This review looks at how AI is being used in different ways, such as analyzing tissue samples, medical images, and complex biological data, to improve the diagnosis of endometrial cancer. The goal is to understand how these new tools work, how they might be used in hospitals, and what challenges still need to be solved. By exploring current research in this area, we hope to highlight the potential of AI to support doctors in making better decisions and to encourage further studies that can bring these tools into everyday medical practice.  \nAbstract: Background: Endometrial cancer (EC) is the most common gynecological malignancy in developed countries, with diagnostic accuracy and early detection being critical to patient outcomes. Recent advances in artificial intelligence (AI) offer new opportunities to enhance diagnostic precision and clinical decision-making. Objectives: This literature review aims to explore recent developments in AI-augmented diagnostic approaches for EC, with a focus on applications in histopathology, imaging, and multi-omics, and to assess their clinical impact and future potential. Methods: A non-systematic literature review was conducted to examine recent advances in artificial intelligence applications for the diagnosis of EC. Relevant studies were identified through searches on PubMed and Google Scholar, focusing on the integration of AI techniques in histopathology, imaging, and multi-omics data. Conclusions: AI-driven diagnostic tools have shown high performance in detecting and characterizing EC across multiple modalities, often matching or exceeding expert-level accuracy. These technologies hold promise for earlier detection, ","cbCaihqDIqak0iBr","https://ap.wps.com/l/cbCaihqDIqak0iBr","pdf",482588,14,"English","# Simple Summary\n# Abstract\n# Keywords\n# 1. Introduction","[{\"question\":\"What is the document’s main goal?\",\"answer\":\"To review recent developments in AI-augmented diagnostic approaches for endometrial cancer and evaluate their clinical impact and future potential.\"},{\"question\":\"Which AI diagnostic areas are covered?\",\"answer\":\"The review focuses on applications in histopathology, imaging, and multi-omics data integration for endometrial cancer diagnosis.\"},{\"question\":\"What conclusions does the review draw about AI tools?\",\"answer\":\"AI-driven diagnostic tools show strong performance across multiple modalities, often matching or exceeding expert-level accuracy, but still require further research and validation for routine clinical use.\"}]","AI-Augmented Advances in the Diagnostic Approaches to Endometrial Cancer | PDF",1790194526,35]