{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,22]],"date-time":"2026-10-22T06:38:03Z","timestamp":1792651083919,"version":"4.5.0"},"reference-count":21,"publisher":"Wiley","license":[{"start":{"date-parts":[[2026,10,21]],"date-time":"2026-10-21T00:00:00Z","timestamp":1792540800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"},{"start":{"date-parts":[[2026,10,21]],"date-time":"2026-10-21T00:00:00Z","timestamp":1792540800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Magnetic Resonance Imaging"],"abstract":"ABSTRACT<\/jats:title>\n \n Background<\/jats:title>\n Retrieving MRI safety conditions for implanted devices is increasingly complex and time\u2010consuming.<\/jats:p>\n <\/jats:sec>\n \n Purpose<\/jats:title>\n To evaluate the accuracy of internet\u2010enabled large language model (LLM) chatbots in retrieving implant\u2010specific MRI safety conditions, using manufacturer documentation as the reference standard.<\/jats:p>\n <\/jats:sec>\n \n Study Type<\/jats:title>\n Prospective comparative evaluation in a constructed device cohort, followed by a retrospective clinical pilot.<\/jats:p>\n <\/jats:sec>\n \n Population<\/jats:title>\n Fifty purposively selected device configurations and 20 patients (10 with cardiac and 10 with noncardiac devices) referred to the MRI Safety Committee.<\/jats:p>\n <\/jats:sec>\n \n Field Strength<\/jats:title>\n No field\u2010strength or sequence restrictions.<\/jats:p>\n <\/jats:sec>\n \n Assessment<\/jats:title>\n Standardized single\u2010shot prompts requested eight parameters from ChatGPT\u20105.0, Gemini 2.5 Pro, and Claude Sonnet 4.5: MRI safety label, field strengths, eligible regions\/centering, transmit\u2010coil restrictions, RF exposure limits, maximum spatial gradient, maximum gradient slew rate, and scan duration. Two radiologists (10 and 25\u2009years' experience) and an MRI technologist (32\u2009years) independently scored 1200 parameter\u2010level outputs against manufacturer Instructions for use and technical manuals using a 5\u2010point scale (5, fully correct; 1, substantively incorrect or fabricated); scores of 4\u20135 indicated accuracy. In the pilot, 160 outputs from ChatGPT\u20105.6, a newer version of the best\u2010performing chatbot, were scored identically.<\/jats:p>\n <\/jats:sec>\n \n Statistical Tests<\/jats:title>\n \n Intraclass correlation coefficient; Gwet's first\u2010order agreement coefficient (AC1); Friedman test; Wilcoxon signed\u2010rank tests; device\u2010clustered bootstrap 95% confidence intervals (CIs); two\u2010sided significance threshold,\n p<\/jats:italic>\n \u2009<\u20090.05.\n <\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n Mean ordinal scores were 4.76 (ChatGPT), 4.20 (Gemini), and 4.17 (Claude); ChatGPT scored significantly higher. Respective pooled parameter\u2010level accuracies were 0.94 (95% CI, 0.89\u20130.98), 0.79 (0.71\u20130.86), and 0.78 (0.69\u20130.87). Pilot accuracy with ChatGPT\u20105.6 was 0.98 (95% CI, 0.94\u20131.00).<\/jats:p>\n <\/jats:sec>\n \n Data Conclusion<\/jats:title>\n Internet\u2010enabled LLM chatbots correctly retrieved up to 94% of MRI safety parameters in the constructed cohort and 98% in the clinical pilot. Errors occurred even with the best\u2010performing chatbot, requiring direct verification against current manufacturer documentation before clinical use.<\/jats:p>\n <\/jats:sec>\n \n Evidence Level<\/jats:title>\n Stage 2.<\/jats:p>\n <\/jats:sec>\n \n Technical Efficacy<\/jats:title>\n Stage 2.<\/jats:p>\n <\/jats:sec>","DOI":"10.1002\/jmri.70578","type":"journal-article","created":{"date-parts":[[2026,10,22]],"date-time":"2026-10-22T05:43:06Z","timestamp":1792647786000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Accuracy of Large Language Model Chatbots in Retrieving\n MRI<\/scp>\n Safety Conditions for Medical Implants"],"prefix":"10.1002","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6430-0948","authenticated-orcid":false,"given":"Ariel","family":"Kerpel","sequence":"first","affiliation":[{"name":"Department of Radiology, Chaim Sheba Medical Center, Tel Hashomer, Israel; Affiliated With the Gray Faculty of Medical and Health Sciences Tel Aviv University Tel Aviv Israel"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shir","family":"Tal","sequence":"additional","affiliation":[{"name":"Department of Radiology, Chaim Sheba Medical Center, Tel Hashomer, Israel; Affiliated With the Gray Faculty of Medical and Health Sciences Tel Aviv University Tel Aviv Israel"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mark","family":"Waserman","sequence":"additional","affiliation":[{"name":"Department of Radiology, Chaim Sheba Medical Center, Tel Hashomer, Israel; Affiliated With the Gray Faculty of Medical and Health Sciences Tel Aviv University Tel Aviv Israel"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Christine","family":"Dan Lansman","sequence":"additional","affiliation":[{"name":"Department of Radiology, Chaim Sheba Medical Center, Tel Hashomer, Israel; Affiliated With the Gray Faculty of Medical and Health Sciences Tel Aviv University Tel Aviv Israel"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4589-5296","authenticated-orcid":false,"given":"Anat","family":"Berkovitch","sequence":"additional","affiliation":[{"name":"Division of Cardiology, Leviev Cardiovascular & Thoracic Center, Chaim Sheba Medical Center, Tel Hashomer, Israel; Affiliated With the Faculty of Medicine and Health Sciences Tel Aviv University Tel Aviv Israel"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Eyal","family":"Klang","sequence":"additional","affiliation":[{"name":"Windreich Department of Artificial Intelligence and Human Health Mount Sinai Medical Center New York New York USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7639-8401","authenticated-orcid":false,"given":"Ronen","family":"Spierer","sequence":"additional","affiliation":[{"name":"Department of Neurology Lady Davis Carmel Medical Center Haifa Israel"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shai","family":"Shrot","sequence":"additional","affiliation":[{"name":"Department of Radiology, Chaim Sheba Medical Center, Tel Hashomer, Israel; Affiliated With the Gray Faculty of Medical and Health Sciences Tel Aviv University Tel Aviv Israel"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Emanuel","family":"Kanal","sequence":"additional","affiliation":[{"name":"Department of Radiology University of Pittsburgh Medical Center Pittsburgh Pennsylvania USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9260-9363","authenticated-orcid":false,"given":"Gahl","family":"Greenberg","sequence":"additional","affiliation":[{"name":"Department of Radiology, Chaim Sheba Medical Center, Tel Hashomer, Israel; Affiliated With the Gray Faculty of Medical and Health Sciences Tel Aviv University Tel Aviv Israel"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,10,21]]},"reference":[{"key":"e_1_2_12_2_1","volume-title":"ACR Manual on MR Safety","author":"American College of Radiology","year":"2026"},{"key":"e_1_2_12_3_1","volume-title":"MRI Bioeffects, Safety, and Patient Management","author":"Shellock F. 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