Journal of the American College of Radiology : JACR最新文献

{"title":"JACR Expert Panel: Artificial Intelligence in Radiology Residency Training.","authors":"Douglas Spaeth-Cook, Morgan P McBee, Margaret C Lin, Peter D Chang, Elias G Kikano","doi":"10.1016/j.jacr.2025.08.003","DOIUrl":"10.1016/j.jacr.2025.08.003","url":null,"abstract":"","PeriodicalId":73968,"journal":{"name":"Journal of the American College of Radiology : JACR","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144849973","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Stop the Drift: Postdeployment Monitoring of Artificial Intelligence in Radiology.","authors":"Erina Quinn, Christoph I Lee","doi":"10.1016/j.jacr.2025.08.002","DOIUrl":"10.1016/j.jacr.2025.08.002","url":null,"abstract":"","PeriodicalId":73968,"journal":{"name":"Journal of the American College of Radiology : JACR","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144849975","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Association of Private Equity and Hospital Consolidation and Negotiated Prices of Radiologic Services.","authors":"Mihir Khunte, Nandita Radhakrishnan, Christopher Whaley, Yashaswini Singh","doi":"10.1016/j.jacr.2025.07.008","DOIUrl":"10.1016/j.jacr.2025.07.008","url":null,"abstract":"<p><strong>Purpose: </strong>The consolidation of radiology practices by hospitals and private equity (PE) firms has accelerated in recent years, reshaping the landscape of radiology practice ownership. There is limited systematic evidence on the growing prevalence of hospital and PE ownership in radiology and its association with negotiated prices for imaging services. The aim of this study was to examine how commercial insurance negotiated prices for radiologic services vary by practice ownership structure, including independent, hospital, and PE-affiliated radiology practices.</p><p><strong>Methods: </strong>A cross-sectional analysis was conducted of radiologists in the United States, categorizing them by practice ownership type. Data from PitchBook were used to identify practices affiliated with PE. Using novel cross-sectional transparency-in-coverage data, negotiated professional fees for radiologic services were compared across hospital, PE-affiliated, and independent radiology practices. Linear regressions were used to examine the association between hospital-employed, PE-affiliated, and independent radiologists and cross-sectional prices paid for physician services, with fixed effects for service, state, and insurers.</p><p><strong>Results: </strong>Among 24,783 radiologists analyzed, 44% were affiliated with independent private practices, 41% were hospital employed, and 11% were PE employed as of 2022. Hospital-employed and PE-affiliated radiologists were concentrated in specific geographic markets. Negotiated professional fees for radiologic services were highest for hospital-employed radiologists, with fees $60.60 (95% confidence interval [CI], $59.53-$61.68) or 43.0% (95% CI, 42.2%-43.7%) higher for hospital-employed radiologists compared with independent radiologists (P < .001). Prices for PE-affiliated practices were $22.39 (95% CI, $20.77-$24.00) or 15.9% (95% CI, 14.7%-17.0%) higher than those for independent practices (P < .001).</p><p><strong>Conclusions: </strong>Hospital and PE-affiliated radiology practices have significantly higher prices for radiologic services compared with independent practices, with hospital-employed radiologists commanding the largest price differentials. These findings highlight the financial implications of ongoing consolidation in radiology and underscore the need for continued research into how these trends affect radiologists, patients, and radiology practices.</p>","PeriodicalId":73968,"journal":{"name":"Journal of the American College of Radiology : JACR","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144981432","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cost-Effectiveness of Opportunistic Osteoporosis Screening Using Chest Radiographs With Deep Learning in the United States.","authors":"Mickael Hiligsmann, Stuart L Silverman, Jean-Yves Reginster","doi":"10.1016/j.jacr.2025.07.028","DOIUrl":"10.1016/j.jacr.2025.07.028","url":null,"abstract":"<p><strong>Objectives: </strong>Deep learning models applied to chest radiographs obtained for other clinical reasons have shown promise in opportunistic osteoporosis screening, particularly among middle-aged to older individuals. This study evaluates the cost-effectiveness of this approach in US women aged 50 years and over.</p><p><strong>Methods: </strong>An economic model, incorporating both a decision tree and a microsimulation Markov model, estimated the cost per quality-adjusted life-year (QALY) gained (in 2024 US dollars) for screening via chest radiographs with deep learning, followed by treatment, versus no screening and treatment. The patient pathways were based on the sensitivity and specificity of artificial intelligence-enhanced radiographs. Real-world medication persistence, realistic assumptions for probabilities of dual-energy x-ray absorptiometry examination postscreening detection and for treatment initiation rates were incorporated. Women with osteoporosis were stratified into high risk (receiving alendronate monotherapy for 5 years) and very high risk (receiving an 18-month anabolic treatment with abaloparatide followed by 5 years of alendronate). Parameter uncertainty was analyzed through sensitivity analyses.</p><p><strong>Results: </strong>The opportunistic screening strategy improved health outcomes, yielding more QALYs and fewer fractures while increasing treatment costs. The cost per QALY gained of opportunistic screening was estimated at $72,085 per QALY gained among women 50+, remaining below the US cost-effectiveness threshold of $100,000 per QALY. Further improvements in cost-effectiveness could be achieved by optimizing follow-up, treatment initiation, and medication adherence.</p><p><strong>Discussion: </strong>This study underscores the cost-effectiveness and public health value of opportunistic, artificial intelligence-driven screening osteoporosis screening using existing chest radiographs, demonstrating its potential to improve early detection and address unmet diagnostic needs in osteoporosis care.</p>","PeriodicalId":73968,"journal":{"name":"Journal of the American College of Radiology : JACR","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144805415","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Communicating Diagnostic Certainty in Radiology Reports: Potential Frameworks From the ACR Commission on Quality and Safety.","authors":"Atul B Shinagare, Prasad R Shankar, Victoria Chernyak, Sean A Woolen, Brian R Herts, Ezana M Azene, Donald G Mitchell, Andrew B Rosenkrantz, Kesav Raghavan, Boaz Karmazyn, Nadja Kadom, Hanna M Zafar, Priya Bhosale, Richard K Do, Daniel A Rodgers, Jennifer C Broder, Mythreyi Chatfield, David B Larson, Matthew S Davenport","doi":"10.1016/j.jacr.2025.07.027","DOIUrl":"10.1016/j.jacr.2025.07.027","url":null,"abstract":"","PeriodicalId":73968,"journal":{"name":"Journal of the American College of Radiology : JACR","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144777017","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Education Equity in Radiology.","authors":"Nishita Sunkara, Harprit Bedi","doi":"10.1016/j.jacr.2025.07.026","DOIUrl":"10.1016/j.jacr.2025.07.026","url":null,"abstract":"<p><p>Educational equity-the principle that all learners should have access to the resources and support they need to achieve their full potential-is a growing area of focus in medical education. In radiology, in which training is deeply reliant on access to advanced technology and specialized faculty, disparities in resources can significantly affect trainee experience and competency. The authors explore the concept of educational equity and its unique relevance to radiology, highlights the challenges faced by lower resourced training programs and review a range of accessible online educational tools that can help mitigate these disparities. The authors offer a call to action for national radiology organizations to reduce barriers by expanding access to educational content, supporting underresourced programs, and protecting graduate medical education funding. Through coordinated efforts, the radiology community can advance equity in training and, ultimately, improve patient care on a global scale.</p>","PeriodicalId":73968,"journal":{"name":"Journal of the American College of Radiology : JACR","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144777018","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"ACR Neck Imaging Reporting and Data System for MRI Version 2025.","authors":"Paul M Bunch, Ashley H Aiken, Kristen L Baugnon, Michael Bazylewicz, Yuh-Shin Chang, Mari Hagiwara, Ashok Srinivasan, Sara B Strauss, Jaime Wicks, David A Zander, Amy F Juliano","doi":"10.1016/j.jacr.2025.07.023","DOIUrl":"10.1016/j.jacr.2025.07.023","url":null,"abstract":"<p><p>Posttreatment surveillance imaging of head and neck cancers poses substantial interpretation challenges because of the anatomic complexities of the region and because of changes from surgical resection, reconstruction, radiation therapy, and chemotherapy. As a result, there is frequent variability in how individual radiologists report the same imaging findings and in the levels of suspicion for recurrence with which different radiologists view identical imaging findings. In response, the ACR formed the Neck Imaging Reporting and Data System (NI-RADS) Committee in 2016, and an ACR-endorsed NI-RADS reporting paradigm was released in 2018 specific to CT and ¹⁸F-fluorodeoxyglucose PET/CT. More recently, the ACR NI-RADS Committee developed category descriptors, imaging findings, and management guidance specific to MRI to directly address surveillance imaging of those head and neck cancer types best served by this imaging modality. In this article, the authors explain ACR NI-RADS MRI version 2025 by discussing the project rationale and consensus process, illustrating the component features, and summarizing the relevant literature and underlying evidence base.</p>","PeriodicalId":73968,"journal":{"name":"Journal of the American College of Radiology : JACR","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144777015","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Artificial Intelligence and Its Impact on Radiology: Summary of the 2024 Intersociety Summer Conference.","authors":"Michael A Bruno, Lars Grimm, Laeton J Pang, Amy Bezold, Brandon K K Fields, Grayson L Baird, Dana Smetherman, Laura Coombs, James V Rawson, Christoph Wald, Ronald V Hublall, Erin S Schwartz, Paul J Chang, Frank J Lexa","doi":"10.1016/j.jacr.2025.07.025","DOIUrl":"10.1016/j.jacr.2025.07.025","url":null,"abstract":"<p><p>The 2024 ACR Intersociety Summer Conference was convened August 9 to 11, 2024, at the Seaport Hotel in Boston, with representation from 27 societies. This year's agenda focused on the anticipated future impacts of artificial intelligence on the practice of radiology. Participants highlighted the need for transparent performance metrics, adaptive regulation, and fundamental changes to enterprise-wide IT infrastructure to support safe and scalable adoption of artificial intelligence in radiology, as well as the need to address potential legal and economic impacts proactively.</p>","PeriodicalId":73968,"journal":{"name":"Journal of the American College of Radiology : JACR","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144777014","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Artificial Intelligence Integration Into Residency Training: How, What, and When?","authors":"Siddhant Dogra, Michael P Recht","doi":"10.1016/j.jacr.2025.07.024","DOIUrl":"10.1016/j.jacr.2025.07.024","url":null,"abstract":"","PeriodicalId":73968,"journal":{"name":"Journal of the American College of Radiology : JACR","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144777016","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Balancing Artificial Intelligence Risks and Benefits in an Evolving Legal Environment.","authors":"Tanya E Karwaki","doi":"10.1016/j.jacr.2025.07.019","DOIUrl":"10.1016/j.jacr.2025.07.019","url":null,"abstract":"","PeriodicalId":73968,"journal":{"name":"Journal of the American College of Radiology : JACR","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144769431","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}