{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,5]],"date-time":"2026-08-05T12:13:35Z","timestamp":1785932015863,"version":"3.56.0"},"reference-count":37,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2026,4,5]],"date-time":"2026-04-05T00:00:00Z","timestamp":1775347200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Medical Sciences"],"abstract":"<jats:p>Background\/Objectives: Malignant salivary gland tumors (MSGTs) are rare, biologically heterogeneous neoplasms in which histopathologic diagnosis and classification are challenging and subject to interobserver variability. Artificial intelligence (AI) approaches using radiologic, histopathologic, and molecular data, including radiomics, deep learning, and biomarker-based models, have been proposed as adjunctive diagnostic tools. This systematic review aimed to identify and critically appraise AI\/ML models across radiologic, histopathologic, and molecular domains for distinct diagnostic tasks in MSGTs, and to integrate their diagnostic roles through a functional meta-synthesis. Methods: We conducted a PRISMA 2020-compliant systematic review. Embase, PubMed\/MEDLINE, and Scopus were searched from inception to February 2026. Eligible studies developed or validated AI\/ML diagnostic or classification models in human salivary gland tumor cohorts and reported extractable performance metrics. Results: From 1265 records, eight studies (1922 participants) met the inclusion criteria, spanning CT\/MRI radiomics or deep learning (n = 4), whole-slide histopathology deep learning (n = 3), and DNA methylation-based classification (n = 1). External validation was reported in two CT-based benign\u2013malignant discrimination studies, with AUCs of 0.890 (95% CI 0.844\u20130.937) and 0.745 (95% CI 0.699\u20130.791). Heterogeneity in model construction, outcome definitions, and validation strategies precluded meta-analysis. Risk of bias was frequently high in QUADAS-2\/PROBAST assessments, driven by retrospective sampling, limited blinding, and analysis-related concerns, while calibration and utility were rarely assessed. Conclusions: AI\/ML models for MSGTs demonstrate promising diagnostic performance, particularly for preoperative benign\u2013malignant discrimination, but the current evidence base is limited by heterogeneity, predominantly internal validation, and high risk of bias. The functional meta-synthesis identified three convergent diagnostic domains: malignancy discrimination, histopathologic subtype classification, and molecular\/epigenetic taxonomy refinement.<\/jats:p>","DOI":"10.3390\/medsci14020183","type":"journal-article","created":{"date-parts":[[2026,4,6]],"date-time":"2026-04-06T01:12:33Z","timestamp":1775437953000},"page":"183","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Artificial-Intelligence-Based Radiologic, Histopathologic, and Molecular Models for the Diagnosis and Classification of Malignant Salivary Gland Tumors: A Systematic Review and Functional Meta-Synthesis"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3663-1416","authenticated-orcid":false,"given":"Carlos M.","family":"Ardila","sequence":"first","affiliation":[{"name":"Department of Periodontics, Saveetha Dental College and Hospitals, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai 600077, India"},{"name":"Biomedical Stomatology Research Group, Basic Sciences Department, Faculty of Dentistry, Universidad de Antioquia (U. de A.), Medell\u00edn 050010, Colombia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2431-7489","authenticated-orcid":false,"given":"Eliana","family":"Pineda-V\u00e9lez","sequence":"additional","affiliation":[{"name":"Biomedical Stomatology Research Group, Basic Sciences Department, Faculty of Dentistry, Universidad de Antioquia (U. de A.), Medell\u00edn 050010, Colombia"},{"name":"Faculty of Dentistry, Instituci\u00f3n Universitaria Visi\u00f3n de las Am\u00e9ricas, Medell\u00edn 050040, Colombia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8631-4910","authenticated-orcid":false,"given":"Anny M.","family":"Vivares-Builes","sequence":"additional","affiliation":[{"name":"Biomedical Stomatology Research Group, Basic Sciences Department, Faculty of Dentistry, Universidad de Antioquia (U. de A.), Medell\u00edn 050010, Colombia"},{"name":"Faculty of Dentistry, Instituci\u00f3n Universitaria Visi\u00f3n de las Am\u00e9ricas, Medell\u00edn 050040, Colombia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alejandro I.","family":"D\u00edaz-Laclaustra","sequence":"additional","affiliation":[{"name":"Department of Basic Sciences, Faculty of Dentistry, Universidad de Antioquia, Medell\u00edn 050040, Colombia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,4,5]]},"reference":[{"key":"ref_1","first-page":"175","article-title":"Salivary gland tumors: 2022 WHO blue book and beyond","volume":"44","year":"2024","journal-title":"Ann. 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