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

{"title":"Expanding Access and Equity in Breast Cancer Screening Through Legislative Action.","authors":"Derek L Nguyen","doi":"10.1016/j.jacr.2025.06.038","DOIUrl":"https://doi.org/10.1016/j.jacr.2025.06.038","url":null,"abstract":"","PeriodicalId":73968,"journal":{"name":"Journal of the American College of Radiology : JACR","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144700566","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":"Imaging Utilization Differences After Telemedicine Versus In-Person Visits.","authors":"YoonKyung Chung, Lauren P Nicola, Gregory N Nicola, Elizabeth Y Rula","doi":"10.1016/j.jacr.2025.05.018","DOIUrl":"https://doi.org/10.1016/j.jacr.2025.05.018","url":null,"abstract":"<p><strong>Purpose: </strong>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.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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.</p><p><strong>Conclusions: </strong>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.</p>","PeriodicalId":73968,"journal":{"name":"Journal of the American College of Radiology : JACR","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144651387","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 Changes in Missouri Law for Breast Cancer Screening, Screening Rates, and Use of Digital Breast Tomosynthesis.","authors":"Amy K Patel, Alexandra R Drake, Eugenia Brandt, Eric W Christensen","doi":"10.1016/j.jacr.2025.06.037","DOIUrl":"https://doi.org/10.1016/j.jacr.2025.06.037","url":null,"abstract":"<p><strong>Objective: </strong>To estimate the association of Missouri law changes expanding mammography coverage to include annual screening from age 40 (previously biennial from age 50), digital breast tomosynthesis (DBT) on overall screening rates, and use of DBT.</p><p><strong>Methods: </strong>This retrospective study (2015-2022) used a national claims database (Inovalon Insights, LLC). A difference-in-differences approach was used in logistic regression models to assess the association of changes in Missouri law with women's likelihood of screening, and for those screened, the likelihood of receiving DBT. Within-Missouri models compared Medicaid and commercial patients to beneficiaries of federally regulated Medicare Advantage (MA) plans. The association was further tested via a comparison of Missouri patients to their counterparts in Missouri border states without a coverage change.</p><p><strong>Results: </strong>There were 1,008,881 unique women (41.4% [417,835] in Missouri). After the Missouri law change, women with Medicaid (odds ratio [OR]: 1.45; 95% confidence interval [CI]: 1.36-1.55) and commercial insurance (OR: 1.05; 95% CI: 1.01-1.10) had a greater increase in likelihood of screening mammography compared to MA women whose coverage is based on federal law. Medicaid women with mammography had a higher likelihood of DBT for both within-Missouri (OR: 2.31; 95% CI: 2.01-2.66) and border-state (OR: 1.24; 95% CI: 1.22-1.27) comparisons. Commercially insured women likewise had a relative increase in likelihood of receiving DBT.</p><p><strong>Conclusion: </strong>Changes in Missouri law expanding breast cancer screening coverage were strongly associated with increased screening rates among Medicaid patients as well as increased likelihood of DBT among screened patients, for both Medicaid and commercially insured patients.</p>","PeriodicalId":73968,"journal":{"name":"Journal of the American College of Radiology : JACR","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144700565","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":"JACR Private Practice Perspective - On-site staffing concerns.","authors":"Ivan DeQuesada, Adam Kaye, Rishi Seth, Andrew K Moriarty","doi":"10.1016/j.jacr.2025.07.005","DOIUrl":"https://doi.org/10.1016/j.jacr.2025.07.005","url":null,"abstract":"","PeriodicalId":73968,"journal":{"name":"Journal of the American College of Radiology : JACR","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144661188","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 effect on Radiology Residency Education: Current Challenges, Opportunities, and Future Directions.","authors":"Joshua Volin, Marly van Assen, Wasif Bala, Nabile Safdar, Patricia Balthazar","doi":"10.1016/j.jacr.2025.07.004","DOIUrl":"https://doi.org/10.1016/j.jacr.2025.07.004","url":null,"abstract":"<p><p>Artificial intelligence has become an impressive force manifesting itself in the radiology field, improving workflows, and influencing clinical decision-making. With this increasing presence, a closer look at how residents can be properly exposed to this technology is needed. Within this paper, we aim to discuss the three pillars central to a trainee's experience including education on AI, AI-Education tools, and clinical implementation of AI. An already overcrowded clinical residency curricula makes little room for a thorough AI education; the challenge of which may be overcome through longitudinal distinct educational tracks during residency or external courses offered through a variety of societies. In addition to teaching the fundamentals of AI, programs which offer education tools utilizing AI will improve on antiquated clinical curricula. These education tools are a growing field in research and industry offering a variety of unique opportunities to promote active inquiry, improved comprehension and overall clinical competence. The near 700 FDA-approved AI clinical tools almost guarantees that residents will be exposed to this technology which may have mixed effects on education, although more research needs to be done to further elucidate this challenge. Ethical considerations, including algorithmic bias, liability, and post-deployment monitoring, highlight the need for structured instruction and mentorship. As AI continues to evolve, residency programs must prioritize evidence-based, adaptable curricula to prepare future radiologists to critically assess, utilize, and contribute to AI advancements, ensuring that these tools complement rather than undermine clinical expertise.</p>","PeriodicalId":73968,"journal":{"name":"Journal of the American College of Radiology : JACR","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144638845","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":"Integrating LLMs into Radiology Education: An Interpretation-Centric Framework for Enhanced Learning While Supporting Workflow.","authors":"Shawn K Lyo, Tessa S Cook","doi":"10.1016/j.jacr.2025.07.003","DOIUrl":"https://doi.org/10.1016/j.jacr.2025.07.003","url":null,"abstract":"<p><p>Radiology education is challenged by increasing clinical workloads, limiting trainee supervision time and hindering real-time feedback. Large language models (LLMs) can enhance radiology education by providing real-time guidance, feedback, and educational resources while supporting efficient clinical workflows. We present an interpretation-centric framework for integrating LLMs into radiology education subdivided into distinct phases spanning pre-dictation preparation, active dictation support, and post-dictation analysis. In the pre-dictation phase, LLMs can analyze clinical data and provide context-aware summaries of each case, suggest relevant educational resources, and triage cases based on their educational value. In the active dictation phase, LLMs can provide real-time educational support through processes such as differential diagnosis support, completeness guidance, classification schema assistance, structured follow-up guidance, and embedded educational resources. In the post-dictation phase, LLMs can be used to analyze discrepancies between trainee and attending reports, identify areas for improvement, provide targeted educational recommendations, track trainee performance over time, and analyze the radiologic entities that trainees encounter. This framework offers a comprehensive approach to integrating LLMs into radiology education, with the potential to enhance trainee learning while preserving clinical efficiency.</p>","PeriodicalId":73968,"journal":{"name":"Journal of the American College of Radiology : JACR","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144638846","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":"Near Peer Mentoring: An Opportunity for Trainees and Departments to Thrive.","authors":"Allison Grayev","doi":"10.1016/j.jacr.2025.07.001","DOIUrl":"10.1016/j.jacr.2025.07.001","url":null,"abstract":"<p><p>Given time and resource constraints in academic medical centers, creation of near peer mentoring opportunities has advantages for all individual and department stakeholders. However, many residents may feel unprepared and uncomfortable when asked to participate in these interactions. After reviewing available literature, advantages for incorporation of near peer mentoring and a suggested framework for incorporation of a mentoring curriculum into residency is provided.</p>","PeriodicalId":73968,"journal":{"name":"Journal of the American College of Radiology : JACR","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144610532","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":"Remote Arbitrage in Radiology: Multiple Affiliations as a Specialty Specific Adaptation to Changing Practice Demands.","authors":"Curtis L Simmons, Laura K Harper, Luis F Goncalves, Richard E Sharpe","doi":"10.1016/j.jacr.2025.06.041","DOIUrl":"10.1016/j.jacr.2025.06.041","url":null,"abstract":"<p><strong>Objective: </strong>To analyze trends from 2017 to 2024 in the number of group and state affiliations among Medicare-serving physicians, with emphasis on diagnostic radiologists.</p><p><strong>Methods: </strong>Affiliation data were obtained from the CMS Physician and Other Supplier Public Use File (2017-2024). Group and state affiliations per physician were analyzed across specialties and compared between diagnostic radiologists and nonradiologist physicians. Statistical analyses included descriptive measures, compound annual growth rates, t tests, correlation, mixed-effect models, and regression models. Subgroup and generational analyses were also conducted.</p><p><strong>Results: </strong>From 2017 to 2024, 635,961 unique physicians were included in this analysis. All physicians had an average of 1.19 group and 1.06 state affiliations. Among physician specialties, diagnostic radiologists had the highest average number of affiliations, with 2.03 groups and 1.31 states on average. This is 1.7 and 1.2 times as many affiliations, respectively, compared with all nonradiologist physicians. From 2017 to 2024, the compound annual growth rates of affiliations per nonradiologist physician was -0.12% for groups and +0.14% for states, whereas for diagnostic radiologists it was +2.23% for groups and +1.63% for states.</p><p><strong>Conclusions: </strong>Compared with all other types of physicians, diagnostic radiologists are much more likely to expand the reach of their services through use of multiple group and state affiliations. Despite recent technological and policy advances with the potential to expand access to care through use of affiliations across groups and states, there has only been modest use of these strategies among other physician types.</p>","PeriodicalId":73968,"journal":{"name":"Journal of the American College of Radiology : JACR","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144577148","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":"LGBTQ+ Inclusion Index Quantifies the Consequences of \"Sunsetting\" Diversity, Equity, and Inclusion.","authors":"Jordan D Perchik, Anne Darrow, Morlie Wang, Erin Cooke, Mary V Marx, Lars J Grimm","doi":"10.1016/j.jacr.2025.06.040","DOIUrl":"10.1016/j.jacr.2025.06.040","url":null,"abstract":"","PeriodicalId":73968,"journal":{"name":"Journal of the American College of Radiology : JACR","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144562197","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":"Using a Large Language Model for Postdeployment Monitoring of FDA-Approved Artificial Intelligence: Pulmonary Embolism Detection Use Case.","authors":"Vera Sorin, Panagiotis Korfiatis, Alex K Bratt, Tim Leiner, Christoph Wald, Crystal Butler, Cole J Cook, Timothy L Kline, Jeremy D Collins","doi":"10.1016/j.jacr.2025.06.036","DOIUrl":"10.1016/j.jacr.2025.06.036","url":null,"abstract":"<p><strong>Background: </strong>Artificial intelligence (AI) is increasingly integrated into clinical workflows. The performance of AI in production can diverge from initial evaluations. Postdeployment monitoring (PDM) remains a challenging ingredient of ongoing quality assurance once AI is deployed in clinical production.</p><p><strong>Purpose: </strong>To develop and evaluate a PDM framework that uses large language models (LLMs) for free-text classification of radiology reports, and human oversight. We demonstrate its application to monitor a commercially vended pulmonary embolism (PE) detection AI (CVPED).</p><p><strong>Methods: </strong>We retrospectively analyzed 11,999 CT pulmonary angiography studies performed between April 30, 2023, and June 17, 2024. Ground truth was determined by combining LLM-based radiology report classification and the CVPED outputs, with human review of discrepancies. We simulated a daily monitoring framework to track discrepancies between CVPED and the LLM. Drift was defined when discrepancy rate exceeded a fixed 95% confidence interval for 7 consecutive days. The confidence interval and the optimal retrospective assessment period were determined from a stable dataset with consistent performance. We simulated drift by systematically altering CVPED or LLM sensitivity and specificity, and we modeled an approach to detect data shifts. We incorporated a human-in-the-loop selective alerting framework for continuous prospective evaluation and to investigate potential for incremental detection.</p><p><strong>Results: </strong>Of 11,999 CT pulmonary angiography studies, 1,285 (10.7%) had PE. Overall, 373 (3.1%) had discrepant classifications between CVPED and LLM. Among 111 CVPED-positive and LLM-negative cases, 29 would have triggered an alert due to the radiologist not interacting with CVPED. Of those, 24 were CVPED false-positives, 1 was an LLM false-negative, and the framework ultimately identified 4 true-alerts for incremental PE cases. The optimal retrospective assessment period for drift detection was determined to be 2 months. A 2% to 3% decline in model specificity caused a 2- to 3-fold increase in discrepancies, and a 10% drop in sensitivity was required to produce a similar effect. For example, a 2.5% drop in LLM specificity led to a 1.7-fold increase in CVPED-negative LLM-positive discrepancies, which would have taken 22 days to detect using the proposed framework.</p><p><strong>Conclusion: </strong>A PDM framework combining LLM-based free-text classification with a human-in-the-loop alerting system can continuously track an image-based AI's performance, alert for performance drift, and provide incremental clinical value.</p>","PeriodicalId":73968,"journal":{"name":"Journal of the American College of Radiology : JACR","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144556047","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}