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

{"title":"Patient-Friendly Summary of the ACR Appropriateness Criteria®: Preoperative and Postoperative Imaging for Bariatric Procedures.","authors":"Sania Choudhary, Sherry S Wang","doi":"10.1016/j.jacr.2025.07.006","DOIUrl":"10.1016/j.jacr.2025.07.006","url":null,"abstract":"","PeriodicalId":73968,"journal":{"name":"Journal of the American College of Radiology : JACR","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144676768","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":"Becoming Artificial Intelligence Native: Preparing Tomorrow's Radiologists.","authors":"Kent Kleinschmidt, Brady Chrisler, Michael Moritz","doi":"10.1016/j.jacr.2025.07.002","DOIUrl":"10.1016/j.jacr.2025.07.002","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":"144669115","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 Moriarity","doi":"10.1016/j.jacr.2025.07.005","DOIUrl":"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":"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 article, 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 that offer education tools using 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 guarantee 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 postdeployment 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, use, 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 Large Language Models 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":"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 predictation preparation, active dictation support, and postdictation analysis. In the predictation 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 postdictation 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":"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}

{"title":"Closing the Loop: From Global Screening to On-Time Access.","authors":"Javier Alejandro Lamprea Ardila, José David Cardona Ortegón, Laura Manuela Olarte Bermúdez, Javier Andrés Romero","doi":"10.1016/j.jacr.2025.06.039","DOIUrl":"10.1016/j.jacr.2025.06.039","url":null,"abstract":"","PeriodicalId":73968,"journal":{"name":"Journal of the American College of Radiology : JACR","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144531484","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":"Radiology Readouts: Faculty and Trainee Perceptions and Preferences of the Current State.","authors":"Shanna A Matalon, Sophia R O'Brien, Jeffrey P Guenette, Scott Simpson","doi":"10.1016/j.jacr.2025.06.035","DOIUrl":"10.1016/j.jacr.2025.06.035","url":null,"abstract":"","PeriodicalId":73968,"journal":{"name":"Journal of the American College of Radiology : JACR","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144531486","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":"Agentic Artificial Intelligence: The Power to Change Medicine and Our World.","authors":"Kimberly Powell, Elliot K Fishman, Linda C Chu, Steven P Rowe, Charles K Crawford","doi":"10.1016/j.jacr.2025.06.032","DOIUrl":"10.1016/j.jacr.2025.06.032","url":null,"abstract":"","PeriodicalId":73968,"journal":{"name":"Journal of the American College of Radiology : JACR","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144512903","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}