{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,3]],"date-time":"2026-10-03T17:05:20Z","timestamp":1791047120517,"version":"4.1.0"},"reference-count":164,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2026,3,13]],"date-time":"2026-03-13T00:00:00Z","timestamp":1773360000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Department of Diagnostic Imaging and Interventional Radiology, Moffitt Cancer Center, Florida Biomedical Research Award","award":["21B12"],"award-info":[{"award-number":["21B12"]}]},{"name":"NIH\/NCI award","award":["U01-CA200464"],"award-info":[{"award-number":["U01-CA200464"]}]},{"DOI":"10.13039\/100000001","name":"NSF Awards","doi-asserted-by":"publisher","award":["2234836"],"award-info":[{"award-number":["2234836"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000001","name":"NSF Awards","doi-asserted-by":"publisher","award":["2234468"],"award-info":[{"award-number":["2234468"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Cancers"],"abstract":"<jats:p>Artificial intelligence (AI) is at the vanguard of transforming radiology in several ways, including augmenting diagnoses, improving workflows, and increasing operational efficiency. Several integration challenges, including concerns over privacy, clinical usability, and workflow compatibility, still remain. This review discusses the foundations and current trends of clinical AI in radiology to provide essential context for ongoing developments. To illustrate translational potential, we describe representative applications, including: (1) local deployment of large language models (LLMs) for restructuring and streamlining radiology reports, improving clarity and consistency without relying on external resources; (2) multimodal AI frameworks combining CT images, clinical data, laboratory biomarkers, and LLM-extracted features from clinical notes for early detection of cachexia in pancreatic cancer; (3) privacy-preserving federated learning (FL) infrastructure enabling collaborative AI model development across institutions without sharing raw patient data; and (4) an uncertainty-aware de-identification pipeline for removing Protected Health Information (PHI) from radiology images and clinical reports to support secure data analysis and sharing. We further discuss emerging opportunities for tumor board decision support, clinical trial matching, radiology report quality assurance, and the development of an imaging complexity index. Collectively, these applications highlight the importance of local deployment, multimodal reasoning, privacy preservation, and human-in-the-loop oversight in translating AI models from research to oncology radiology practice.<\/jats:p>","DOI":"10.3390\/cancers18060942","type":"journal-article","created":{"date-parts":[[2026,3,13]],"date-time":"2026-03-13T11:12:25Z","timestamp":1773400345000},"page":"942","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Clinical AI in Radiology: Foundations, Trends, Applications, and Emerging Directions"],"prefix":"10.3390","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9896-872X","authenticated-orcid":false,"given":"Iryna","family":"Hartsock","sequence":"first","affiliation":[{"name":"Department of Machine Learning, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL 33612, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nikolas","family":"Koutsoubis","sequence":"additional","affiliation":[{"name":"Department of Machine Learning, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL 33612, USA"},{"name":"Department of Electrical Engineering, University of South Florida, Tampa, FL 33620, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0456-3073","authenticated-orcid":false,"given":"Sabeen","family":"Ahmed","sequence":"additional","affiliation":[{"name":"Department of Machine Learning, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL 33612, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8947-7942","authenticated-orcid":false,"given":"Nathan","family":"Parker","sequence":"additional","affiliation":[{"name":"Department of Health Outcomes and Behavior, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL 33612, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3241-3216","authenticated-orcid":false,"given":"Matthew B.","family":"Schabath","sequence":"additional","affiliation":[{"name":"Department of Cancer Epidemiology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL 33612, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cyrillo","family":"Araujo","sequence":"additional","affiliation":[{"name":"Department of Machine Learning, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL 33612, USA"},{"name":"Department of Diagnostic Imaging & Interventional Radiology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL 33612, USA"},{"name":"Department of Oncologic Sciences, University of South Florida, Tampa, FL 33612, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aliya","family":"Qayyum","sequence":"additional","affiliation":[{"name":"Department of Machine Learning, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL 33612, USA"},{"name":"Department of Diagnostic Imaging & Interventional Radiology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL 33612, USA"},{"name":"Department of Oncologic Sciences, University of South Florida, Tampa, FL 33612, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cesar","family":"Lam","sequence":"additional","affiliation":[{"name":"Department of Machine Learning, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL 33612, USA"},{"name":"Department of Diagnostic Imaging & Interventional Radiology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL 33612, USA"},{"name":"Department of Oncologic Sciences, University of South Florida, Tampa, FL 33612, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1621-1510","authenticated-orcid":false,"given":"Robert A.","family":"Gatenby","sequence":"additional","affiliation":[{"name":"Department of Diagnostic Imaging & Interventional Radiology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL 33612, USA"},{"name":"Department of Oncologic Sciences, University of South Florida, Tampa, FL 33612, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8551-0090","authenticated-orcid":false,"given":"Ghulam","family":"Rasool","sequence":"additional","affiliation":[{"name":"Department of Machine Learning, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL 33612, USA"},{"name":"Department of Diagnostic Imaging & Interventional Radiology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL 33612, USA"},{"name":"Department of Oncologic Sciences, University of South Florida, Tampa, FL 33612, USA"},{"name":"Department of Neuro-Oncology, H. 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