{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,24]],"date-time":"2026-10-24T15:01:11Z","timestamp":1792854071685,"version":"4.5.0"},"reference-count":36,"publisher":"Elsevier BV","issue":"12","license":[{"start":{"date-parts":[[2025,12,1]],"date-time":"2025-12-01T00:00:00Z","timestamp":1764547200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2025,12,1]],"date-time":"2025-12-01T00:00:00Z","timestamp":1764547200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"}],"funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000054","name":"National Cancer Institute","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000054","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000070","name":"National Institute of Biomedical Imaging and Bioengineering","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000070","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100008980","name":"Soci\u00e9t\u00e9 Fran\u00e7aise de Radiologie","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100008980","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000098","name":"National Institutes of Health Clinical Center","doi-asserted-by":"publisher","award":["1ZID BC011242"],"award-info":[{"award-number":["1ZID BC011242"]}],"id":[{"id":"10.13039\/100000098","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000098","name":"National Institutes of Health Clinical Center","doi-asserted-by":"publisher","award":["Z1A CL040015"],"award-info":[{"award-number":["Z1A CL040015"]}],"id":[{"id":"10.13039\/100000098","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["clinicalkey.com","clinicalkey.com.au","clinicalkey.es","clinicalkey.fr","clinicalkey.jp","jvir.org","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Journal of Vascular and Interventional Radiology"],"published-print":{"date-parts":[[2025,12]]},"DOI":"10.1016\/j.jvir.2025.09.017","type":"journal-article","created":{"date-parts":[[2025,11,21]],"date-time":"2025-11-21T16:30:12Z","timestamp":1763742612000},"page":"1929-1936.e1","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":4,"title":["Artificial Intelligence and Intelligent Interventional Radiology: Fusion of Human and Machine"],"prefix":"10.1016","volume":"36","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2856-4794","authenticated-orcid":false,"given":"Hannah","family":"Huth","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Laetitia","family":"Saccenti","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"James","family":"Anibal","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ifechi","family":"Ukeh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6065-8523","authenticated-orcid":false,"given":"Michael","family":"Kassin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Judy","family":"Gichoya","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2273-7962","authenticated-orcid":false,"given":"Nicole","family":"Varble","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ming","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0574-4231","authenticated-orcid":false,"given":"John","family":"Karanian","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Baris","family":"Turkbey","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4297-0051","authenticated-orcid":false,"given":"Bradford J.","family":"Wood","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.jvir.2025.09.017_bib1","doi-asserted-by":"crossref","first-page":"228","DOI":"10.3390\/diagnostics13020228","article-title":"Technological advancements in interventional oncology","volume":"13","author":"Posa","year":"2023","journal-title":"Diagnostics (Basel)"},{"key":"10.1016\/j.jvir.2025.09.017_bib2","doi-asserted-by":"crossref","DOI":"10.3389\/fradi.2024.1403761","article-title":"Value of interventional radiology and their contributions to modern medical systems","volume":"4","author":"Campbell","year":"2024","journal-title":"Front Radiol"},{"key":"10.1016\/j.jvir.2025.09.017_bib3","doi-asserted-by":"crossref","DOI":"10.1155\/2019\/6153041","article-title":"Artificial intelligence in interventional radiology: a literature review and future perspectives","volume":"2019","author":"Iezzi","year":"2019","journal-title":"J Oncol"},{"key":"10.1016\/j.jvir.2025.09.017_bib4","doi-asserted-by":"crossref","first-page":"558","DOI":"10.1177\/08465371241236376","article-title":"An introductory guide to artificial intelligence in interventional radiology: part 1 foundational knowledge","volume":"75","author":"Warren","year":"2024","journal-title":"Can Assoc Radiol J"},{"key":"10.1016\/j.jvir.2025.09.017_bib5","doi-asserted-by":"crossref","first-page":"568","DOI":"10.1177\/08465371241236377","article-title":"An introductory guide to artificial intelligence in interventional radiology: part 2: implementation considerations and harms","volume":"75","author":"Warren","year":"2024","journal-title":"Can Assoc Radiol J"},{"key":"10.1016\/j.jvir.2025.09.017_bib6","doi-asserted-by":"crossref","first-page":"375","DOI":"10.2217\/fon-2021-0471","article-title":"Implementing multidisciplinary tumor boards in oncology: a narrative review","volume":"18","author":"Mano","year":"2022","journal-title":"Future Oncol"},{"key":"10.1016\/j.jvir.2025.09.017_bib7","doi-asserted-by":"crossref","first-page":"1639","DOI":"10.21037\/tlcr.2019.11.03","article-title":"A review on the impact of lung cancer multidisciplinary care on patient outcomes","volume":"9","author":"Heinke","year":"2020","journal-title":"Transl Lung Cancer Res"},{"key":"10.1016\/j.jvir.2025.09.017_bib8","doi-asserted-by":"crossref","first-page":"585","DOI":"10.1007\/s00270-024-03945-0","article-title":"IR-GPT: AI foundation models to optimize interventional radiology","volume":"48","author":"Brenner","year":"2025","journal-title":"Cardiovasc Intervent Radiol"},{"key":"10.1016\/j.jvir.2025.09.017_bib9","doi-asserted-by":"crossref","DOI":"10.3389\/fonc.2022.892620","article-title":"Virtual biopsy in soft tissue sarcoma. How close are we?","volume":"12","author":"Arthur","year":"2022","journal-title":"Front Oncol"},{"key":"10.1016\/j.jvir.2025.09.017_bib10","doi-asserted-by":"crossref","first-page":"799","DOI":"10.1186\/s12967-024-05609-6","article-title":"Artificial intelligence: illuminating the depths of the tumor microenvironment","volume":"22","author":"Xie","year":"2024","journal-title":"J Transl Med"},{"key":"10.1016\/j.jvir.2025.09.017_bib11","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1186\/s12943-024-02101-z","article-title":"Biomarkers for diagnosis and therapeutic options in hepatocellular carcinoma","volume":"23","author":"Chan","year":"2024","journal-title":"Mol Cancer"},{"key":"10.1016\/j.jvir.2025.09.017_bib12","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1186\/s12943-022-01543-7","article-title":"Liquid biopsy: a step closer to transform diagnosis, prognosis and future of cancer treatments","volume":"21","author":"Lone","year":"2022","journal-title":"Mol Cancer"},{"key":"10.1016\/j.jvir.2025.09.017_bib13","doi-asserted-by":"crossref","DOI":"10.1177\/11769351221076062","article-title":"Liquid biopsy: a distinctive approach to the diagnosis and prognosis of cancer","volume":"21","author":"Hirahata","year":"2022","journal-title":"Cancer Inform"},{"key":"10.1016\/j.jvir.2025.09.017_bib14","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1309\/YN14-K8U4-5FLJ-DGJE","article-title":"Is fine-needle aspiration biopsy a practical alternative to open biopsy for the primary diagnosis of sarcoma? Experience with 140 patients","volume":"115","author":"Kilpatrick","year":"2001","journal-title":"Am J Clin Pathol"},{"key":"10.1016\/j.jvir.2025.09.017_bib15","article-title":"A machine learning model to predict hepatocellular carcinoma response to transcatheter arterial chemoembolization","volume":"1","author":"Morshid","year":"2019","journal-title":"Radiol Artif Intell"},{"key":"10.1016\/j.jvir.2025.09.017_bib16","doi-asserted-by":"crossref","first-page":"347","DOI":"10.1038\/s43018-023-00694-w","article-title":"The artificial intelligence-based model ANORAK improves histopathological grading of lung adenocarcinoma","volume":"5","author":"Pan","year":"2024","journal-title":"Nat Cancer"},{"key":"10.1016\/j.jvir.2025.09.017_bib17","doi-asserted-by":"crossref","first-page":"1779","DOI":"10.2147\/JMDH.S410301","article-title":"Machine learning and AI in cancer prognosis, prediction, and treatment selection: a critical approach","volume":"16","author":"Zhang","year":"2023","journal-title":"J Multidiscip Healthc"},{"key":"10.1016\/j.jvir.2025.09.017_bib18","doi-asserted-by":"crossref","first-page":"7161","DOI":"10.1007\/s00330-024-10767-8","article-title":"Pre-operative lung ablation prediction using deep learning","volume":"34","author":"Keshavamurthy","year":"2024","journal-title":"Eur Radiol"},{"key":"10.1016\/j.jvir.2025.09.017_bib19","doi-asserted-by":"crossref","first-page":"920","DOI":"10.1109\/JBHI.2013.2287202","article-title":"Treatment planning and image guidance for radiofrequency ablation of large tumors","volume":"18","author":"Ren","year":"2014","journal-title":"IEEE J Biomed Health Inform"},{"key":"10.1016\/j.jvir.2025.09.017_bib20","doi-asserted-by":"crossref","first-page":"632","DOI":"10.1111\/hpb.12423","article-title":"The first clinical application of planning software for laparoscopic microwave thermosphere ablation of malignant liver tumours","volume":"17","author":"Berber","year":"2015","journal-title":"HPB (Oxford)"},{"key":"10.1016\/j.jvir.2025.09.017_bib21","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1016\/j.euros.2023.05.018","article-title":"Prediction and mapping of intraprostatic tumor extent with artificial intelligence","volume":"54","author":"Priester","year":"2023","journal-title":"Eur Urol Open Sci"},{"key":"10.1016\/j.jvir.2025.09.017_bib23","doi-asserted-by":"crossref","first-page":"1558","DOI":"10.1016\/j.jvir.2024.06.033","article-title":"Variations in microwave ablation zones as a function of probe spacing, angulation, and geometry","volume":"35","author":"Huth","year":"2024","journal-title":"J Vasc Interv Radiol"},{"key":"10.1016\/j.jvir.2025.09.017_bib24","doi-asserted-by":"crossref","first-page":"215","DOI":"10.3390\/diagnostics14020215","article-title":"An automatic needle puncture path-planning method for thermal ablation of lung tumors","volume":"14","author":"Wang","year":"2024","journal-title":"Diagnostics (Basel)"},{"key":"10.1016\/j.jvir.2025.09.017_bib25","doi-asserted-by":"crossref","first-page":"514","DOI":"10.1097\/SLA.0000000000006263","article-title":"A pilot study using machine-learning algorithms and wearable technology for the early detection of postoperative complications after cardiothoracic surgery","volume":"281","author":"Beqari","year":"2025","journal-title":"Ann Surg"},{"key":"10.1016\/j.jvir.2025.09.017_bib26","author":"Rosenfield"},{"key":"10.1016\/j.jvir.2025.09.017_bib27","unstructured":"Gupta K. Mechanical thrombectomy, artificial intelligence and the activation of a pulmonary embolism response team. In pertconsortium.org [Poster]. PE Symposium, United States of America."},{"key":"10.1016\/j.jvir.2025.09.017_bib28","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2023.103022","article-title":"C-DARL: contrastive diffusion adversarial representation learning for label-free blood vessel segmentation","volume":"91","author":"Kim","year":"2024","journal-title":"Med Image Anal"},{"key":"10.1016\/j.jvir.2025.09.017_bib29","series-title":"Virtual parenchymal perfusion for selective intra-arterial therapy of liver cancer. 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI)","first-page":"1147","author":"Lesage","year":"2016"},{"key":"10.1016\/j.jvir.2025.09.017_bib30","doi-asserted-by":"crossref","first-page":"3510","DOI":"10.1007\/s00330-023-09390-w","article-title":"Accuracy and reproducibility of a cone beam CT-based virtual parenchymal perfusion algorithm in the prediction of SPECT\/CT anatomical and volumetric results during the planification of radioembolization for HCC","volume":"33","author":"Derbel","year":"2023","journal-title":"Eur Radiol"},{"key":"10.1016\/j.jvir.2025.09.017_bib31","doi-asserted-by":"crossref","first-page":"1434","DOI":"10.1007\/s11547-021-01392-2","article-title":"Whole-body magnetic resonance imaging (WB-MRI) for cancer screening: recommendations for use","volume":"126","author":"Petralia","year":"2021","journal-title":"Radiol Med"},{"key":"10.1016\/j.jvir.2025.09.017_bib32","doi-asserted-by":"crossref","first-page":"855","DOI":"10.1093\/europace\/euac246","article-title":"360\u00b0 virtual reality to improve patient education and reduce anxiety towards atrial fibrillation ablation","volume":"25","author":"Hermans","year":"2023","journal-title":"Europace"},{"key":"10.1016\/j.jvir.2025.09.017_bib33","doi-asserted-by":"crossref","first-page":"184","DOI":"10.1016\/j.avsg.2019.01.015","article-title":"The application of virtual reality in patient education","volume":"59","author":"Pandrangi","year":"2019","journal-title":"Ann Vasc Surg"},{"key":"10.1016\/j.jvir.2025.09.017_bib34","first-page":"481","article-title":"Virtual reality for patient education about hypertension: a randomized pilot study","volume":"10","author":"Jiravska Godula","year":"2023","journal-title":"J Cardiovasc Dev Dis"},{"key":"10.1016\/j.jvir.2025.09.017_bib35","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1186\/s41747-024-00452-2","article-title":"Artificial intelligence in interventional radiology: state of the art","volume":"8","author":"Glielmo","year":"2024","journal-title":"Eur Radiol Exp"},{"key":"10.1016\/j.jvir.2025.09.017_bib36","doi-asserted-by":"crossref","DOI":"10.1016\/j.tvir.2024.101009","article-title":"Fusion Technologies for image-guided robotic interventions","volume":"27","author":"Ukeh","year":"2024","journal-title":"Tech Vasc Interv Radiol"},{"key":"10.1016\/j.jvir.2025.09.017_bib37","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1038\/s41591-021-01506-3","article-title":"Federated learning for predicting clinical outcomes in patients with COVID-19","volume":"27","author":"Dayan","year":"2021","journal-title":"Nat Med"}],"container-title":["Journal of Vascular and Interventional Radiology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1051044325006050?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1051044325006050?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T06:44:29Z","timestamp":1773816269000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1051044325006050"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12]]},"references-count":36,"journal-issue":{"issue":"12","published-print":{"date-parts":[[2025,12]]}},"alternative-id":["S1051044325006050"],"URL":"https:\/\/doi.org\/10.1016\/j.jvir.2025.09.017","relation":{},"ISSN":["1051-0443"],"issn-type":[{"value":"1051-0443","type":"print"}],"subject":[],"published":{"date-parts":[[2025,12]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Artificial Intelligence and Intelligent Interventional Radiology: Fusion of Human and Machine","name":"articletitle","label":"Article Title"},{"value":"Journal of Vascular and Interventional Radiology","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.jvir.2025.09.017","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"simple-article","name":"content_type","label":"Content Type"},{"value":"Published by Elsevier, Inc., on behalf of SIR.","name":"copyright","label":"Copyright"}]}}