{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T23:53:03Z","timestamp":1782949983390,"version":"3.54.5"},"reference-count":298,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,4,17]],"date-time":"2026-04-17T00:00:00Z","timestamp":1776384000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Immunol."],"abstract":"<jats:p>Gynecological diseases represent a persistent global health burden. According to a WHO report, the global incidence of gynecological diseases exceeds 65%. Furthermore, over 90% of women suffer from gynecological issues to varying degrees. They account for about 4.5% of the global disease burden. This share is higher than that of other major global health problems. For example, malaria accounts for about 1.04%. Tuberculosis accounts for about 1.9%. Ischemic heart disease accounts for about 2.2%. Mounting evidence suggests that inflammasomes act as master regulators linking chronic inflammation with the onset and progression of gynecological disorders including endometriosis, ovarian cancer, and polycystic ovary syndrome. Yet, traditional two-dimensional cultures and animal models fail to reproduce the intricate immune\u2013endocrine microenvironment of the human reproductive system, impeding translational progress. This review provides an integrated perspective that unites inflammasome biology, organoid technology, and artificial intelligence (AI) into a new research paradigm for precision immunology. We comprehensively summarize the molecular mechanisms of inflammasome activation\u2014particularly NLRP3, AIM2, and IFI16\u2014and their dual roles in inflammatory injury and immune regulation across gynecological diseases. We further highlight advances in organoid-based models that reconstruct the three-dimensional architecture and immune context of reproductive tissues, offering a physiologically relevant platform for mechanistic exploration. In parallel, AI-driven multi-omics analytics and image-based deep learning are shown to accelerate data interpretation, reveal inflammasome-related regulatory networks, and optimize experimental design. By bridging immunopathology, bioengineering, and computational intelligence, this review establishes a conceptual and methodological framework for studying inflammasome-mediated pathogenesis in gynecological diseases. It highlights the transformative capabilities of AI-assisted organoid systems in identifying biomarkers, predicting therapeutic responses, and enabling individualized immunomodulatory strategies\u2014laying a foundation for next-generation translational research in reproductive immunology. To provide a comprehensive overview of recent advances in this field, we conducted a literature search primarily using the PubMed database, with keywords including \u201cinflammasome,\u201d \u201cartificial intelligence,\u201d \u201corganoid,\u201d and \u201cgynecological inflammation,\u201d as well as related terms. The review mainly focuses on experimental and methodological studies published within the past five years, aiming to reflect the most current developments in this rapidly evolving area.<\/jats:p>","DOI":"10.3389\/fimmu.2026.1753651","type":"journal-article","created":{"date-parts":[[2026,4,17]],"date-time":"2026-04-17T04:13:18Z","timestamp":1776399198000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Inflammasomes meet organoids and artificial intelligence: unraveling the complexity of gynecological inflammation"],"prefix":"10.3389","volume":"17","author":[{"given":"Lanyue","family":"Zhang","sequence":"first","affiliation":[{"name":"Clinical Medical College, Southwest Medical University","place":["Luzhou, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiangnan","family":"Zhao","sequence":"additional","affiliation":[{"name":"Clinical Medical College, Southwest Medical 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