Yunan Wu, Michael Iorga, Suvarna Badhe, James Zhang, Donald R Cantrell, Elaine J Tanhehco, Nicholas Szrama, Andrew M Naidech, Michael Drakopoulos, Shamis T Hasan, Kunal M Patel, Tarek A Hijaz, Eric J Russell, Shamal Lalvani, Amit Adate, Todd B Parrish, Aggelos K Katsaggelos, Virginia B Hill
Rohan Bareja, Marwa Ismail, Douglas Martin, Ameya Nayate, Ipsa Yadav, Murad Labbad, Prateek Dullur, Sanya Garg, Benita Tamrazi, Ralph Salloum, Ashley Margol, Alexander Judkins, Sukanya Raj Iyer, Peter de Blank, Pallavi Tiwari
Hao Li, Han Liu, Heinrich von Busch, Robert Grimm, Henkjan Huisman, Angela Tong, David Winkel, Tobias Penzkofer, Ivan Shabunin, Moon Hyung Choi, Qingsong Yang, Dieter Szolar, Steven Shea, Fergus Coakley, Mukesh Harisinghani, Ipek Oguz, Dorin Comaniciu, Ali Kamen, Bin Lou
Louis Gagnon, Diviya Gupta, George Mastorakos, Nathan White, Vanessa Goodwill, Carrie R McDonald, Thomas Beaumont, Christopher Conlin, Tyler M Seibert, Uyen Nguyen, Jona Hattangadi-Gluth, Santosh Kesari, Jessica D Schulte, David Piccioni, Kathleen M Schmainda, Nikdokht Farid, Anders M Dale, Jeffrey D Rudie
Bastien Le Guellec, Alexandre Lefèvre, Charlotte Geay, Lucas Shorten, Cyril Bruge, Lotfi Hacein-Bey, Philippe Amouyel, Jean-Pierre Pruvo, Gregory Kuchcinski, Aghiles Hamroun
{"title":"Performance of an Open-Source Large Language Model in Extracting Information from Free-Text Radiology Reports.","authors":"Bastien Le Guellec, Alexandre Lefèvre, Charlotte Geay, Lucas Shorten, Cyril Bruge, Lotfi Hacein-Bey, Philippe Amouyel, Jean-Pierre Pruvo, Gregory Kuchcinski, Aghiles Hamroun","doi":"10.1148/ryai.230364","DOIUrl":"10.1148/ryai.230364","url":null,"abstract":"<p><p>Purpose To assess the performance of a local open-source large language model (LLM) in various information extraction tasks from real-life emergency brain MRI reports. Materials and Methods All consecutive emergency brain MRI reports written in 2022 from a French quaternary center were retrospectively reviewed. Two radiologists identified MRI scans that were performed in the emergency department for headaches. Four radiologists scored the reports' conclusions as either normal or abnormal. Abnormalities were labeled as either headache-causing or incidental. Vicuna (LMSYS Org), an open-source LLM, performed the same tasks. Vicuna's performance metrics were evaluated using the radiologists' consensus as the reference standard. Results Among the 2398 reports during the study period, radiologists identified 595 that included headaches in the indication (median age of patients, 35 years [IQR, 26-51 years]; 68% [403 of 595] women). A positive finding was reported in 227 of 595 (38%) cases, 136 of which could explain the headache. The LLM had a sensitivity of 98.0% (95% CI: 96.5, 99.0) and specificity of 99.3% (95% CI: 98.8, 99.7) for detecting the presence of headache in the clinical context, a sensitivity of 99.4% (95% CI: 98.3, 99.9) and specificity of 98.6% (95% CI: 92.2, 100.0) for the use of contrast medium injection, a sensitivity of 96.0% (95% CI: 92.5, 98.2) and specificity of 98.9% (95% CI: 97.2, 99.7) for study categorization as either normal or abnormal, and a sensitivity of 88.2% (95% CI: 81.6, 93.1) and specificity of 73% (95% CI: 62, 81) for causal inference between MRI findings and headache. Conclusion An open-source LLM was able to extract information from free-text radiology reports with excellent accuracy without requiring further training. <b>Keywords:</b> Large Language Model (LLM), Generative Pretrained Transformers (GPT), Open Source, Information Extraction, Report, Brain, MRI <i>Supplemental material is available for this article.</i> Published under a CC BY 4.0 license. See also the commentary by Akinci D'Antonoli and Bluethgen in this issue.</p>","PeriodicalId":29787,"journal":{"name":"Radiology-Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":8.1,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11294959/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140877470","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Ali S Tejani, Michail E Klontzas, Anthony A Gatti, John T Mongan, Linda Moy, Seong Ho Park, Charles E Kahn
{"title":"Checklist for Artificial Intelligence in Medical Imaging (CLAIM): 2024 Update.","authors":"Ali S Tejani, Michail E Klontzas, Anthony A Gatti, John T Mongan, Linda Moy, Seong Ho Park, Charles E Kahn","doi":"10.1148/ryai.240300","DOIUrl":"10.1148/ryai.240300","url":null,"abstract":"","PeriodicalId":29787,"journal":{"name":"Radiology-Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":8.1,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11304031/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141162489","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}