Journal of the American College of Radiology最新文献

{"title":"The ACR Learning Network Recommendations Follow-Up Improvement Collaborative: Ensuring Quality Surveillance of Pulmonary Nodules","authors":"Ben Wandtke MD, MS ,&nbsp;Denes Szekeres MD ,&nbsp;Kandice Garcia Tomkins MS ,&nbsp;Kay Zacharias-Andrews MBA ,&nbsp;Katherine Hall ,&nbsp;Steve J. Stephen MBA ,&nbsp;Mythreyi Chatfield PhD ,&nbsp;David Larson MD, MBA","doi":"10.1016/j.jacr.2025.05.017","DOIUrl":"10.1016/j.jacr.2025.05.017","url":null,"abstract":"<div><h3>Purpose</h3><div>Our purpose was to share findings from the first two cohorts of the ACR Learning Network Recommendations Follow-Up Improvement Collaborative. The collaborative targets safe, high-quality practice in the follow-up of incidental pulmonary nodules. Participating sites had the shared goal of improving (1) recommendation adherence to the Fleischner Society Guidelines and (2) follow-up imaging completion rates.</div></div><div><h3>Methods</h3><div>The quality improvement<span> initiative was structured around the ACR ImPower Program (ACR, Reston, Virginia), which incorporates elements of shared learning between concurrently participating sites and longitudinal cohorts. Selected sites assembled teams, developed specific goals, benchmarked their performance, identified root causes for low recommendation follow-up, and implemented unique interventions. Performance and strategies were shared at set intervals between sites during the implementation phase and at the conclusion of the project.</span></div></div><div><h3>Results</h3><div>Summary data reflect findings from seven sites representing academic, private, and community practices. There were variable degrees of success between participating sites for both outcomes, with some achieving significant improvement and others facing challenges. Across all sites, adherence to the guidelines improved from a baseline of 62.3% ± 24.2% to 89.0% ± 16.8%. Similarly, the percentage of patients undergoing recommended follow-up imaging increased from 41.4% ± 25.4% to 61.1% ± 23.9%.</div></div><div><h3>Conclusions</h3><div>A longitudinal, shared learning approach yielded tangible but variable improvements in the management of incidental pulmonary nodules. The most successful sites employed both robust tracking systems and dedicated care coordination teams to ensure appropriate and timely follow-up. These findings suggest that a synergistic combination of people, process, and technological interventions perform better than any single intervention alone.</div></div>","PeriodicalId":49044,"journal":{"name":"Journal of the American College of Radiology","volume":"22 9","pages":"Pages 1071-1081"},"PeriodicalIF":5.1,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144175677","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"AI in Action: A Road Map From the Radiology AI Council for Effective Model Evaluation and Deployment","authors":"Hari Trivedi MD ,&nbsp;Bardia Khosravi MD, MPH, MHPE ,&nbsp;Judy Gichoya MD, MS ,&nbsp;Laura Benson ,&nbsp;Damian Dyckman MD, PhD ,&nbsp;James Galt PhD ,&nbsp;Brian M. Howard MD ,&nbsp;Elias G. Kikano MD ,&nbsp;Jean Kunjummen DO ,&nbsp;Neil Lall MD ,&nbsp;Xiao T. Li MD ,&nbsp;Sumir Patel MD ,&nbsp;Nabile Safdar MD, MPH ,&nbsp;Ninad Salastekar MD, MPH ,&nbsp;Colin Segovis MD, PhD ,&nbsp;Marly van Assen PhD ,&nbsp;Peter Harri MD","doi":"10.1016/j.jacr.2025.05.016","DOIUrl":"10.1016/j.jacr.2025.05.016","url":null,"abstract":"<div><div>As the integration of artificial intelligence (AI) into radiology workflows continues to evolve, establishing standardized processes for the evaluation and deployment of AI models is crucial to ensure success. This article outlines the creation of a Radiology AI Council at a large academic center and subsequent development of framework in the form of a rubric to formalize the evaluation of radiology AI models and onboard them into clinical workflows. The rubric aims to address the challenges faced during the deployment of AI models, such as real-world model performance, workflow implementation, resource allocation, return on investment, and impact to the broader health system. Using this comprehensive rubric, the council aims to ensure that the process for selecting AI models is both standardized and transparent. This article outlines the steps taken to establish this rubric, its components, and the initial results from evaluation of 13 models over an 8-month period. We emphasize the importance of holistic model evaluation beyond performance metrics, and transparency and objectivity in AI model evaluation, with the goal of improving the efficacy and safety of AI models in radiology.</div></div>","PeriodicalId":49044,"journal":{"name":"Journal of the American College of Radiology","volume":"22 9","pages":"Pages 1041-1049"},"PeriodicalIF":5.1,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144144710","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"When Confidence Overpowers Competence: The Dunning-Kruger Effect in Radiology","authors":"Subha Ghosh MD, MBA","doi":"10.1016/j.jacr.2025.05.002","DOIUrl":"10.1016/j.jacr.2025.05.002","url":null,"abstract":"","PeriodicalId":49044,"journal":{"name":"Journal of the American College of Radiology","volume":"22 9","pages":"Pages 1091-1094"},"PeriodicalIF":5.1,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144036201","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Patient-Friendly Summary of the ACR Appropriateness Criteria®: Imaging of Suspected Intracranial Hypotension","authors":"Corey Feuer BA ,&nbsp;Luke Ledbetter MD","doi":"10.1016/j.jacr.2025.05.005","DOIUrl":"10.1016/j.jacr.2025.05.005","url":null,"abstract":"","PeriodicalId":49044,"journal":{"name":"Journal of the American College of Radiology","volume":"22 9","pages":"Page 1107"},"PeriodicalIF":5.1,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144014623","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Imaging Utilization Differences After Telemedicine Versus In-Person Visits","authors":"YoonKyung Chung PhD ,&nbsp;Lauren P. Nicola MD ,&nbsp;Gregory N. Nicola MD ,&nbsp;Elizabeth Y. Rula PhD","doi":"10.1016/j.jacr.2025.05.018","DOIUrl":"10.1016/j.jacr.2025.05.018","url":null,"abstract":"<div><h3>Purpose</h3><div>The COVID-19 pandemic resulted in a rapid expansion of telemedicine, but little is known about its impact on diagnostic imaging utilization. This study evaluates differences in imaging utilization after telemedicine versus in-person visits at the national level.</div></div><div><h3>Methods</h3><div>This was a retrospective case-control study using data from Optum’s de-identified Clinformatics® Data Mart Database. Telemedicine and in-person visits during the year 2021 were identified and matched on various visit and patient characteristics. Weighted multivariate linear models with coarsened exact matching weights were estimated to quantify the differences in post-visit imaging utilization rates and number of imaging studies among visits with any post-visit imaging between the two visit types within 7 days, 14 days, and 30 days.</div></div><div><h3>Results</h3><div>Of 23,431,032 visits, 10% were telemedicine visits. The 7-day post-visit imaging utilization rate was 2.4 percentage points (β = −2.37 [95% confidence interval: −2.41 to −2.32]) lower among telemedicine visits, representing a 29.7% lower imaging utilization rate after a telemedicine visit compared with an in-person visit. Results were similar for the 14- and 30-day periods. Among the subset of visits with post-visit imaging, the number of imaging studies within 7 days after the visit was 0.02 (β = 0.021 [95% confidence interval: 0.015-0.027]) higher (1.5% relative difference) for telemedicine versus in-person visits. This small difference persisted across all periods.</div></div><div><h3>Conclusions</h3><div>Telemedicine visits were associated with lower imaging utilization compared with matched in-person office visits, indicating this setting may not contribute to imaging growth. Future studies should explore the appropriateness of imaging ordered during telemedicine visits.</div></div>","PeriodicalId":49044,"journal":{"name":"Journal of the American College of Radiology","volume":"22 9","pages":"Pages 998-1007"},"PeriodicalIF":5.1,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144651387","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Global Radiology: Pearls From a Medical Student Who Rotated in the United States, Australia, and India","authors":"Anisha Mittal MD, MEd","doi":"10.1016/j.jacr.2025.05.015","DOIUrl":"10.1016/j.jacr.2025.05.015","url":null,"abstract":"","PeriodicalId":49044,"journal":{"name":"Journal of the American College of Radiology","volume":"22 9","pages":"Pages 1069-1070"},"PeriodicalIF":5.1,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144133364","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Impact of a Worklist Reprioritization Initiative to Improve Report Availability for Same-Day Imaging and Clinic Visits","authors":"Caroline Peers BS ,&nbsp;Ronilda Lacson MD, PhD ,&nbsp;M. Stephen Ledbetter MD, MPH ,&nbsp;Andrew J. Wagner MD, PhD ,&nbsp;Catherine S. Giess MD ,&nbsp;Ramin Khorasani MD, MPH ,&nbsp;Pamela J. DiPiro MD","doi":"10.1016/j.jacr.2025.01.010","DOIUrl":"10.1016/j.jacr.2025.01.010","url":null,"abstract":"","PeriodicalId":49044,"journal":{"name":"Journal of the American College of Radiology","volume":"22 9","pages":"Pages 1027-1031"},"PeriodicalIF":5.1,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143076729","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Large Language Models for Global Health Clinics: Opportunities and Challenges","authors":"Satvik Tripathi ,&nbsp;Dana Alkhulaifat MD ,&nbsp;Meghana Muppuri MD ,&nbsp;Ameena Elahi MPA, RT(R), CIIR ,&nbsp;Farouk Dako MD, MPH","doi":"10.1016/j.jacr.2025.04.007","DOIUrl":"10.1016/j.jacr.2025.04.007","url":null,"abstract":"<div><div>Large language models (LLMs) have emerged as a new wave of artificial intelligence, and their applications could emerge as a pivotal resource capable of reshaping health care communication, research, and informed decision-making processes. These models offer unprecedented potential to swiftly disseminate critical health information and transcend linguistic barriers. However, their integration into health care systems presents formidable challenges, including inherent biases in training data, privacy vulnerabilities, and disparities in digital literacy. Despite these obstacles, LLMs possess unparalleled analytic prowess to inform evidence-based health care policies and clinical practices. Addressing these challenges necessitates the formulation of robust ethical frameworks, bias mitigation strategies, and educational initiatives to ensure equitable access to health care resources globally. By navigating these complexities with meticulous attention and foresight, LLMs stand poised to catalyze substantial advancements in global health outcomes, promoting health equity and improving population health worldwide.</div></div>","PeriodicalId":49044,"journal":{"name":"Journal of the American College of Radiology","volume":"22 8","pages":"Pages 917-923"},"PeriodicalIF":5.1,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144058129","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Table of Content","authors":"","doi":"10.1016/S1546-1440(25)00376-X","DOIUrl":"10.1016/S1546-1440(25)00376-X","url":null,"abstract":"","PeriodicalId":49044,"journal":{"name":"Journal of the American College of Radiology","volume":"22 8","pages":"Pages A1-A4"},"PeriodicalIF":5.1,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144749925","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Navigating Moral Injury in Radiology: Adapting to the Winds of Change","authors":"Alessandro Furlan MD, MMM,&nbsp;Jules H. Sumkin DO","doi":"10.1016/j.jacr.2025.05.014","DOIUrl":"10.1016/j.jacr.2025.05.014","url":null,"abstract":"","PeriodicalId":49044,"journal":{"name":"Journal of the American College of Radiology","volume":"22 8","pages":"Page 860"},"PeriodicalIF":5.1,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144129382","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}