Pub Date : 2026-03-01Epub Date: 2025-09-11DOI: 10.1016/j.jacr.2025.09.011
Elsa Zhang BSc , Michael Dang MSDS , Joseph H. Joo MD, MS , Ching-Ching Claire Lin PhD , Joshua M. Liao MD, MSc
{"title":"National Adoption of Artificial Intelligence Software in Medicare Among Radiologists","authors":"Elsa Zhang BSc , Michael Dang MSDS , Joseph H. Joo MD, MS , Ching-Ching Claire Lin PhD , Joshua M. Liao MD, MSc","doi":"10.1016/j.jacr.2025.09.011","DOIUrl":"10.1016/j.jacr.2025.09.011","url":null,"abstract":"","PeriodicalId":49044,"journal":{"name":"Journal of the American College of Radiology","volume":"23 3","pages":"Pages 430-433"},"PeriodicalIF":5.1,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145058761","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}
Pub Date : 2026-03-01Epub Date: 2025-07-31DOI: 10.1016/j.jacr.2025.07.019
Tanya E. Karwaki JD, PhD
{"title":"Balancing Artificial Intelligence Risks and Benefits in an Evolving Legal Environment","authors":"Tanya E. Karwaki JD, PhD","doi":"10.1016/j.jacr.2025.07.019","DOIUrl":"10.1016/j.jacr.2025.07.019","url":null,"abstract":"","PeriodicalId":49044,"journal":{"name":"Journal of the American College of Radiology","volume":"23 3","pages":"Pages 375-377"},"PeriodicalIF":5.1,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144769431","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}
Pub Date : 2026-03-01Epub Date: 2025-10-06DOI: 10.1016/j.jacr.2025.09.032
Chintan Shah MD, MS , Satyam Ghodasara MD , David Chen PhD , Po-Hao Chen MD, MBA
The adoption of artificial intelligence (AI) into clinical practice in radiology can be facilitated by following a structured pipeline for implementation. In this article, we propose a practical framework for the responsible implementation of AI through four phases: validation, deployment, value assessment, and postdeployment surveillance. Validation involves retrospective or offline testing on institutional data to assess the model’s local performance. Deployment progresses through limited trial and full deployment stages, with an emphasis on workflow considerations, integrations, operational metrics, and stakeholder feedback. Value assessment is longitudinal throughout these phases and encompasses both financial and nonfinancial returns on investment. Finally, ongoing surveillance can detect data drift, monitor clinical performance, and maintain AI safety. The framework proposed herein provides a governance-oriented approach to AI implementation, addressing the core questions: Does it work? Does it help? Does it stay?
{"title":"Does It Work, Help, and Stay? A Framework for Implementing Artificial Intelligence Tools in Radiology","authors":"Chintan Shah MD, MS , Satyam Ghodasara MD , David Chen PhD , Po-Hao Chen MD, MBA","doi":"10.1016/j.jacr.2025.09.032","DOIUrl":"10.1016/j.jacr.2025.09.032","url":null,"abstract":"<div><div>The adoption of artificial intelligence (AI) into clinical practice in radiology can be facilitated by following a structured pipeline for implementation. In this article, we propose a practical framework for the responsible implementation of AI through four phases: validation, deployment, value assessment, and postdeployment surveillance. Validation involves retrospective or offline testing on institutional data to assess the model’s local performance. Deployment progresses through limited trial and full deployment stages, with an emphasis on workflow considerations, integrations, operational metrics, and stakeholder feedback. Value assessment is longitudinal throughout these phases and encompasses both financial and nonfinancial returns on investment. Finally, ongoing surveillance can detect data drift, monitor clinical performance, and maintain AI safety. The framework proposed herein provides a governance-oriented approach to AI implementation, addressing the core questions: Does it work? Does it help? Does it stay?</div></div>","PeriodicalId":49044,"journal":{"name":"Journal of the American College of Radiology","volume":"23 3","pages":"Pages 378-388"},"PeriodicalIF":5.1,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145253826","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}
Pub Date : 2026-03-01Epub Date: 2025-10-14DOI: 10.1016/j.jacr.2025.10.019
Stacy D. O’Connor MD, MPH , Tarik Alkasab MD, PhD , Joel K.R. Samuel MD , Dorothy A. Sippo MD, MPH
Actionable findings requiring follow-up with additional imaging or other diagnostic procedures are frequently reported for a wide variety of radiology examinations. Completion of recommended follow-up can lead to new diagnoses including cancer. However, recommended follow-up completion is inconsistent, particularly when follow-up is for findings unrelated to the initial reason for the examination. Follow-up recommendation tracking systems, using a combination of information technology tools and human navigators, can facilitate completion of recommended follow-up, but often require significant effort for manual chart review and direct communication with providers and patients. Artificial intelligence, including large language models able to process vast and diverse unstructured text data, offer the opportunity to improve efficiency with data extraction and aggregation tasks, like those required for follow-up recommendation management. In this review article, we will review the key components of follow-up recommendation management systems: (1) identification of follow-up recommendations within radiology reports, (2) communication of these recommendations, (3) tracking of follow-up recommendations to completion, and (4) outcomes tracking. For each component, we will explore how artificial intelligence can improve efficiency and expand capabilities of robust management systems that ensure the loop is closed for follow-up recommendations.
{"title":"The Potential Role of Artificial Intelligence in Systematic Follow-Up Recommendation Tracking and Outcome Assessment","authors":"Stacy D. O’Connor MD, MPH , Tarik Alkasab MD, PhD , Joel K.R. Samuel MD , Dorothy A. Sippo MD, MPH","doi":"10.1016/j.jacr.2025.10.019","DOIUrl":"10.1016/j.jacr.2025.10.019","url":null,"abstract":"<div><div>Actionable findings requiring follow-up with additional imaging or other diagnostic procedures are frequently reported for a wide variety of radiology examinations. Completion of recommended follow-up can lead to new diagnoses including cancer. However, recommended follow-up completion is inconsistent, particularly when follow-up is for findings unrelated to the initial reason for the examination. Follow-up recommendation tracking systems, using a combination of information technology tools and human navigators, can facilitate completion of recommended follow-up, but often require significant effort for manual chart review and direct communication with providers and patients. Artificial intelligence, including large language models able to process vast and diverse unstructured text data, offer the opportunity to improve efficiency with data extraction and aggregation tasks, like those required for follow-up recommendation management. In this review article, we will review the key components of follow-up recommendation management systems: (1) identification of follow-up recommendations within radiology reports, (2) communication of these recommendations, (3) tracking of follow-up recommendations to completion, and (4) outcomes tracking. For each component, we will explore how artificial intelligence can improve efficiency and expand capabilities of robust management systems that ensure the loop is closed for follow-up recommendations.</div></div>","PeriodicalId":49044,"journal":{"name":"Journal of the American College of Radiology","volume":"23 3","pages":"Pages 410-420"},"PeriodicalIF":5.1,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145310266","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}
Pub Date : 2026-03-01Epub Date: 2025-11-06DOI: 10.1016/j.jacr.2025.10.005
Ryan C. Lee BS , Roham Hadidchi BS , Michael C. Coard MS , Yossef Rubinov BS , Tharun Alamuri BS , Aliena Liaw BS , Rahul Chandrupatla MD , Tim Q. Duong PhD
Large language models (LLMs) are increasingly being explored for a wide range of applications in radiology, offering the potential to enhance clinical workflows, improve diagnostic accuracy, and support patient communication. In this scoping review the authors examine the current and emerging uses of LLMs on radiology text, focusing on areas such as report generation, structured data extraction, workflow optimization, and clinical decision support. A literature search was conducted on PubMed and Embase, and a total of 69 articles were included in the review. The capabilities and limitations of existing approaches were assessed, and key methodologic considerations were discussed, including transparency and bias, while identifying critical gaps in validation and generalizability. Overall, LLMs demonstrated strong performance in workflows such as report simplification and translation but produced mixed results in classification tasks. Certain methods such as fine-tuning and structured prompt generation improved LLM accuracy. In assessing the characteristics of the included studies, although most studies performed well in documenting the independence of their testing and training datasets and LLM prompting methods, fewer than half of studies explicitly attempted to manage the inherent stochasticity of LLMs. By synthesizing recent advancements and outlining future directions, the aim of this review was to guide clinicians, researchers, and health care stakeholders in responsibly harnessing the transformative potential of LLMs in radiologic care.
{"title":"Use of Large Language Models on Radiology Reports: A Scoping Review","authors":"Ryan C. Lee BS , Roham Hadidchi BS , Michael C. Coard MS , Yossef Rubinov BS , Tharun Alamuri BS , Aliena Liaw BS , Rahul Chandrupatla MD , Tim Q. Duong PhD","doi":"10.1016/j.jacr.2025.10.005","DOIUrl":"10.1016/j.jacr.2025.10.005","url":null,"abstract":"<div><div>Large language models (LLMs) are increasingly being explored for a wide range of applications in radiology, offering the potential to enhance clinical workflows, improve diagnostic accuracy, and support patient communication. In this scoping review the authors examine the current and emerging uses of LLMs on radiology text, focusing on areas such as report generation, structured data extraction, workflow optimization, and clinical decision support. A literature search was conducted on PubMed and Embase, and a total of 69 articles were included in the review. The capabilities and limitations of existing approaches were assessed, and key methodologic considerations were discussed, including transparency and bias, while identifying critical gaps in validation and generalizability. Overall, LLMs demonstrated strong performance in workflows such as report simplification and translation but produced mixed results in classification tasks. Certain methods such as fine-tuning and structured prompt generation improved LLM accuracy. In assessing the characteristics of the included studies, although most studies performed well in documenting the independence of their testing and training datasets and LLM prompting methods, fewer than half of studies explicitly attempted to manage the inherent stochasticity of LLMs. By synthesizing recent advancements and outlining future directions, the aim of this review was to guide clinicians, researchers, and health care stakeholders in responsibly harnessing the transformative potential of LLMs in radiologic care.</div></div>","PeriodicalId":49044,"journal":{"name":"Journal of the American College of Radiology","volume":"23 3","pages":"Pages 437-454"},"PeriodicalIF":5.1,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145454134","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}
Pub Date : 2026-03-01Epub Date: 2025-12-09DOI: 10.1016/j.jacr.2025.12.012
Omar Msto Hussain Nasser MD , Brian W. Bresnahan PhD , Nathan M. Cross MD, MS, CIIP , James V. Rawson MD
Multiple barriers have been identified to developing a learning health care systems (LHSs) including organizational culture, data systems and interoperability, funding and workforce limitations, and regulatory challenges. Artificial intelligence (AI) is being explored both inside and outside of health care, with varying degrees of scientific rigor in the testing of AI applications. LHSs and AI face similar implementation challenges, which presents an opportunity for synergy. By reviewing AI use cases from the lens of how it can be used to reduce previously identified barriers to progressing toward an LHS, opportunities for facilitating this journey can be identified. AI tools can impact both clinical and nonclinical business processes. The process of testing and implementing AI tools based on high-quality evidence or signal should prespecify thresholds and expectations of incremental effectiveness (marginal risk-benefit) improvement compared with current standards of care, as is standard in health services research, quality improvement, process improvement, and best practices of comparative health care research. Business process examples to improve workflow using AI tools may adhere to less rigorous evidentiary standards compared with tools guiding patient-centered clinical decision scenarios, such as with AI-based diagnostic applications. This review indicates that AI tools provide tremendous opportunities for radiology to improve health care systems, workflow processes, and patients’ health outcomes.
{"title":"Review of Artificial Intelligence Business Cases to Advance Toward Learning Health Care Systems","authors":"Omar Msto Hussain Nasser MD , Brian W. Bresnahan PhD , Nathan M. Cross MD, MS, CIIP , James V. Rawson MD","doi":"10.1016/j.jacr.2025.12.012","DOIUrl":"10.1016/j.jacr.2025.12.012","url":null,"abstract":"<div><div>Multiple barriers have been identified to developing a learning health care systems (LHSs) including organizational culture, data systems and interoperability, funding and workforce limitations, and regulatory challenges. Artificial intelligence (AI) is being explored both inside and outside of health care, with varying degrees of scientific rigor in the testing of AI applications. LHSs and AI face similar implementation challenges, which presents an opportunity for synergy. By reviewing AI use cases from the lens of how it can be used to reduce previously identified barriers to progressing toward an LHS, opportunities for facilitating this journey can be identified. AI tools can impact both clinical and nonclinical business processes. The process of testing and implementing AI tools based on high-quality evidence or signal should prespecify thresholds and expectations of incremental effectiveness (marginal risk-benefit) improvement compared with current standards of care, as is standard in health services research, quality improvement, process improvement, and best practices of comparative health care research. Business process examples to improve workflow using AI tools may adhere to less rigorous evidentiary standards compared with tools guiding patient-centered clinical decision scenarios, such as with AI-based diagnostic applications. This review indicates that AI tools provide tremendous opportunities for radiology to improve health care systems, workflow processes, and patients’ health outcomes.</div></div>","PeriodicalId":49044,"journal":{"name":"Journal of the American College of Radiology","volume":"23 3","pages":"Pages 399-409"},"PeriodicalIF":5.1,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145745969","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}
Pub Date : 2026-03-01Epub Date: 2026-03-02DOI: 10.1016/S1546-1440(26)00024-4
{"title":"Table of Content","authors":"","doi":"10.1016/S1546-1440(26)00024-4","DOIUrl":"10.1016/S1546-1440(26)00024-4","url":null,"abstract":"","PeriodicalId":49044,"journal":{"name":"Journal of the American College of Radiology","volume":"23 3","pages":"Pages A1-A4"},"PeriodicalIF":5.1,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147415515","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}
Pub Date : 2026-03-01Epub Date: 2025-09-26DOI: 10.1016/j.jacr.2025.07.036
Deniz Esin Tekcan Sanli MD, Ahmet Necati Sanli
{"title":"Strengthening the Evidence Base for Interpretation-Centric Large Language Model Integration in Radiology Education","authors":"Deniz Esin Tekcan Sanli MD, Ahmet Necati Sanli","doi":"10.1016/j.jacr.2025.07.036","DOIUrl":"10.1016/j.jacr.2025.07.036","url":null,"abstract":"","PeriodicalId":49044,"journal":{"name":"Journal of the American College of Radiology","volume":"23 3","pages":"Page 341"},"PeriodicalIF":5.1,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145187799","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}
Pub Date : 2026-03-01Epub Date: 2025-12-09DOI: 10.1016/j.jacr.2025.12.008
Vrushab Gowda MD, JD , Christoph I. Lee MD, MS, MBA
{"title":"Large Language Models in Radiology Practice: Looking Beyond the Hype","authors":"Vrushab Gowda MD, JD , Christoph I. Lee MD, MS, MBA","doi":"10.1016/j.jacr.2025.12.008","DOIUrl":"10.1016/j.jacr.2025.12.008","url":null,"abstract":"","PeriodicalId":49044,"journal":{"name":"Journal of the American College of Radiology","volume":"23 3","pages":"Pages 455-456"},"PeriodicalIF":5.1,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145745974","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}
Pub Date : 2026-03-01Epub Date: 2025-09-10DOI: 10.1016/j.jacr.2025.09.005
Cindy Yuan MD, PhD , Heidi A. Edmonson PhD , Colin Segovis MD, PhD
{"title":"Current Procedural Terminology Coding for MR Safety Evaluation: Implementation Tips","authors":"Cindy Yuan MD, PhD , Heidi A. Edmonson PhD , Colin Segovis MD, PhD","doi":"10.1016/j.jacr.2025.09.005","DOIUrl":"10.1016/j.jacr.2025.09.005","url":null,"abstract":"","PeriodicalId":49044,"journal":{"name":"Journal of the American College of Radiology","volume":"23 3","pages":"Pages 434-436"},"PeriodicalIF":5.1,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145056444","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}
扫码关注我们
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号