{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,7]],"date-time":"2026-08-07T15:23:42Z","timestamp":1786116222860,"version":"build-2736575974"},"reference-count":49,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2026,8,6]],"date-time":"2026-08-06T00:00:00Z","timestamp":1785974400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>The analysis of non-blood biological fluids, including cerebrospinal fluid (CSF), serous effusions, and synovial fluid, plays a central role in laboratory medicine by providing essential diagnostic and prognostic information for neurological, infectious, inflammatory, and neoplastic diseases. However, the interpretation of these specimens remains challenging because it requires the integration of heterogeneous biochemical, cytological, microbiological, molecular, and clinical data, often in the absence of standardized analytical workflows. Artificial intelligence (AI), particularly Machine Learning (ML) and Deep Learning (DL), is emerging as a powerful approach for extracting clinically relevant information from complex multidimensional datasets beyond the capabilities of conventional analytical methods. AI-driven Clinical Decision-Support Systems (CDSSs) can integrate laboratory findings with clinical, demographic, imaging, and multi-omics data, supporting diagnostic interpretation, patient stratification, and personalized clinical decision-making. At the same time, the convergence of AI with proteomics, metabolomics, metagenomics, and other omics technologies is accelerating biomarker discovery and advancing precision laboratory medicine. Current evidence indicates different levels of maturity across biological fluids. AI-assisted interpretation of CSF biomarkers and digital cytology of serous effusions currently show the strongest clinical evidence, whereas applications involving synovial fluid and integrated multi-omics remain largely exploratory. Although important technical, methodological, and regulatory challenges still limit widespread clinical implementation, AI has the potential to improve diagnostic accuracy, reduce interpretative variability, and support more integrated diagnostic workflows. This mini-review summarizes current and emerging AI applications in non-blood biological fluid analysis, with particular emphasis on biomarker discovery, CDSS, multi-omics integration, current evidence, existing limitations, and future perspectives for precision laboratory medicine.<\/jats:p>","DOI":"10.3390\/diagnostics16152478","type":"journal-article","created":{"date-parts":[[2026,8,6]],"date-time":"2026-08-06T08:08:02Z","timestamp":1786003682000},"page":"2478","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Artificial Intelligence for Integrated Analysis of Non-Blood Biological Fluids: From Biomarker Discovery to Clinical Decision-Support Systems"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2711-0088","authenticated-orcid":false,"given":"Valentina","family":"Becherucci","sequence":"first","affiliation":[{"name":"Clinical Pathology Laboratory, Santa Maria Annunziata Hospital, Azienda USL Toscana Centro, Bagno a Ripoli, 50012 Florence, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Francesca","family":"Romano","sequence":"additional","affiliation":[{"name":"General Laboratory, Azienda Ospedaliero-Universitaria Careggi, 50134 Florence, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Edda","family":"Russo","sequence":"additional","affiliation":[{"name":"Clinical Pathology Laboratory Unit, San. Giuseppe Hospital, Azienda USL Toscana Centro, 50053 Empoli, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,8,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"693","DOI":"10.1097\/WCO.0000000000001317","article-title":"Blood and cerebrospinal fluid biomarkers in neuro-oncology","volume":"37","author":"Pellerino","year":"2024","journal-title":"Curr. Opin. Neurol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1195","DOI":"10.1080\/14737159.2023.2276918","article-title":"Required improvements for cerebrospinal fluid-based biomarker tests of Alzheimer\u2019s disease","volume":"23","author":"Gaetani","year":"2023","journal-title":"Expert Rev. Mol. Diagn."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Naik, A., Adeleye, O., Koester, S.W., Winkler, E.A., Hartke, J.N., Karahalios, K., Mihaljevic, S., Rani, A., Raikwar, S., and Rulney, J.D. (2023). 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