{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,24]],"date-time":"2026-08-24T14:31:50Z","timestamp":1787581910919,"version":"build-2736575974"},"reference-count":81,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2026,8,24]],"date-time":"2026-08-24T00:00:00Z","timestamp":1787529600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Microorganisms"],"abstract":"<jats:p>Chronic non-communicable conditions\u2014type 1 and type 2 diabetes mellitus (T1DM, T2DM), metabolic obesity syndrome (MOS), polycystic ovary syndrome (PCOS), colorectal and extra-intestinal cancers, and systemic autoimmune disease\u2014share a prolonged asymptomatic phase during which conventional screening is invasive, insensitive, or resource-intensive. This review synthesizes the 2021\u20132026 literature on fecal microbiome-based artificial intelligence (AI) diagnostics across these conditions, extracting reported discrimination, validation strategy, microbial and short-chain fatty acid (SCFA) biomarkers, and cross-cohort reproducibility. Across the primary classifier studies tabulated here, reported areas under the curve (AUCs) span 0.76\u20130.99 under internal validation but 0.69\u20130.91 under external or cross-population validation; in the four studies reporting both, the median AUC falls from 0.875 to 0.810. Verified external-validation values include 0.82 for colorectal cancer, 0.79 for T2DM and 0.792 for discrimination of systemic lupus erythematosus from rheumatoid arthritis and controls. Clinical readiness turns on this internal-to-external gap more than on the headline AUC. We propose a multimodal deep learning architecture coupled with explainable AI; no component has been implemented or evaluated on data, and it is presented as a design proposal. Fecal-microbiome-based multimodal AI is technically feasible but clinically unvalidated, pending prospective, harmonized cross-cohort trials.<\/jats:p>","DOI":"10.3390\/microorganisms14091880","type":"journal-article","created":{"date-parts":[[2026,8,24]],"date-time":"2026-08-24T13:20:43Z","timestamp":1787577643000},"page":"1880","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Toward AI-Driven Detection of Asymptomatic Chronic Conditions from Stool Metagenomics and Dietary Data: A Multimodal Deep Learning Framework for T1DM, T2DM, MOS\/PCOS, Cancer, and Autoimmune Disease"],"prefix":"10.3390","volume":"14","author":[{"given":"K\u00e1roly","family":"Szili","sequence":"first","affiliation":[{"name":"Department of Obstetrics and Gynecology, Faculty of Health and Sport Sciences Sz\u00e9chenyi Istv\u00e1n University of Gy\u0151r, Egyetem t\u00e9r 1, 9026 Gy\u0151r, Hungary"},{"name":"Department of Obstetrics and Gynecology, Faculty of Medicine, University of Szeged, 6720 Szeged, Hungary"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7343-9856","authenticated-orcid":false,"given":"Csilla","family":"D\u00e9zsi","sequence":"additional","affiliation":[{"name":"Department of Obstetrics and Gynecology, Faculty of Health and Sport Sciences Sz\u00e9chenyi Istv\u00e1n University of Gy\u0151r, Egyetem t\u00e9r 1, 9026 Gy\u0151r, Hungary"},{"name":"Department of Obstetrics and Gynecology, Faculty of Medicine, University of Szeged, 6720 Szeged, Hungary"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-5334-4493","authenticated-orcid":false,"given":"Viktor","family":"Guly\u00e1s-Oldal","sequence":"additional","affiliation":[{"name":"Department of Obstetrics and Gynecology, Faculty of Health and Sport Sciences Sz\u00e9chenyi Istv\u00e1n University of Gy\u0151r, Egyetem t\u00e9r 1, 9026 Gy\u0151r, Hungary"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"D\u00e1niel","family":"Sallai","sequence":"additional","affiliation":[{"name":"Department of Obstetrics and Gynecology, Faculty of Health and Sport Sciences Sz\u00e9chenyi Istv\u00e1n University of Gy\u0151r, Egyetem t\u00e9r 1, 9026 Gy\u0151r, Hungary"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-6994-5219","authenticated-orcid":false,"given":"G\u00e1bor","family":"Patay","sequence":"additional","affiliation":[{"name":"Department of Obstetrics and Gynecology, Faculty of Health and Sport Sciences Sz\u00e9chenyi Istv\u00e1n University of Gy\u0151r, Egyetem t\u00e9r 1, 9026 Gy\u0151r, Hungary"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-9576-5243","authenticated-orcid":false,"given":"Ekaterine","family":"Paschali","sequence":"additional","affiliation":[{"name":"Department of Obstetrics and Gynecology, Faculty of Health and Sport Sciences Sz\u00e9chenyi Istv\u00e1n University of Gy\u0151r, Egyetem t\u00e9r 1, 9026 Gy\u0151r, Hungary"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7054-8333","authenticated-orcid":false,"given":"S\u00e1ndor","family":"Nagy","sequence":"additional","affiliation":[{"name":"Department of Obstetrics and Gynecology, Faculty of Health and Sport Sciences Sz\u00e9chenyi Istv\u00e1n University of Gy\u0151r, Egyetem t\u00e9r 1, 9026 Gy\u0151r, Hungary"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,8,24]]},"reference":[{"key":"ref_1","unstructured":"GBD 2021 Causes of Death Collaborators (2024). 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