Pub Date : 2026-01-01DOI: 10.1016/j.jacr.2025.09.006
Gul Moonis MD , Suyash Mohan MD, MBBS , Prachi Dubey MBBS , Daniel T. Ginat MD, MS , Peter Kralt MD , Pallavi S. Utukuri MD , Noushin Yahyavi-Firouz-Abadi MD, MBA , Jeffrey N. Bruce MD , Jenny K. Hoang MBBS, MBA , Pari V. Pandharipande MD , Stella K. Kang MD, MS
The ACR Incidental Findings Committee presents recommendations for managing incidental pineal cysts on CT of the head or MRI of the brain. The Pineal Cyst Subcommittee is composed of neuroradiologists and a neurosurgeon who developed the algorithms presented. These recommendations represent a combination of current published evidence as well as expert experience and opinion and were finalized by a formal consensus-building process. The recommendations address commonly encountered incidental findings in the pineal gland and are not intended to be a comprehensive review of all pineal incidental findings. The goal is to improve the quality of care by providing guidance on management of incidentally detected pineal cysts.
{"title":"Management of Incidentally Discovered Pineal Cyst on CT and MRI: Recommendations from the ACR Incidental Findings Committee","authors":"Gul Moonis MD , Suyash Mohan MD, MBBS , Prachi Dubey MBBS , Daniel T. Ginat MD, MS , Peter Kralt MD , Pallavi S. Utukuri MD , Noushin Yahyavi-Firouz-Abadi MD, MBA , Jeffrey N. Bruce MD , Jenny K. Hoang MBBS, MBA , Pari V. Pandharipande MD , Stella K. Kang MD, MS","doi":"10.1016/j.jacr.2025.09.006","DOIUrl":"10.1016/j.jacr.2025.09.006","url":null,"abstract":"<div><div>The ACR Incidental Findings Committee presents recommendations for managing incidental pineal cysts on CT of the head or MRI of the brain. The Pineal Cyst Subcommittee is composed of neuroradiologists and a neurosurgeon who developed the algorithms presented. These recommendations represent a combination of current published evidence as well as expert experience and opinion and were finalized by a formal consensus-building process. The recommendations address commonly encountered incidental findings in the pineal gland and are not intended to be a comprehensive review of all pineal incidental findings. The goal is to improve the quality of care by providing guidance on management of incidentally detected pineal cysts.</div></div>","PeriodicalId":49044,"journal":{"name":"Journal of the American College of Radiology","volume":"23 1","pages":"Pages 117-122"},"PeriodicalIF":5.1,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145483803","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 : 2026-01-01DOI: 10.1016/j.jacr.2025.06.030
Jack Smith , Elliot K. Fishman MD , Linda C. Chu MD , Steven P. Rowe MD, PhD , Charles K. Crawford BS
{"title":"From Automation to Innovation: How Artificial Intelligence Is Reshaping Global Industries","authors":"Jack Smith , Elliot K. Fishman MD , Linda C. Chu MD , Steven P. Rowe MD, PhD , Charles K. Crawford BS","doi":"10.1016/j.jacr.2025.06.030","DOIUrl":"10.1016/j.jacr.2025.06.030","url":null,"abstract":"","PeriodicalId":49044,"journal":{"name":"Journal of the American College of Radiology","volume":"23 1","pages":"Pages 136-138"},"PeriodicalIF":5.1,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144340763","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 : 2026-01-01DOI: 10.1016/j.jacr.2025.10.003
Tariq Rashid MD , Aditya Khurana MD , Alan Zhu BS , David Supeck DO, PhD , Laura Harper MD , Curtis Simmons MD, MBA , Richard E. Sharpe Jr. MD, MBA
Purpose
The aim of this study was to evaluate breast imaging (BI) productivity trends in the US Medicare population from 2013 to 2022, including subspecialized interpretation trends overall, and by imaging modality.
Methods
Outpatient imaging claims for the US Medicare population from 2013 to 2022 were extracted from CMS databases, categorized as BI or not and by imaging modality. Total work relative value units (wRVUs) and wRVUs for BI studies were summed, and year-specific BI case mixes were calculated per radiologist. Radiologists interpreting more than 50% of their case mix as BI each year were classified as subspecialized breast radiologists. Productivity and subspecialization trends overall and by imaging modality were evaluated.
Results
From 2013 to 2022, BI wRVUs increased by 7.8% annually, and radiologists performing BI services decreased by 2.2% annually. The percentage of all radiologists submitting BI claims decreased from 46.5% to 34.2%. The number of subspecialized breast radiologists submitting claims for BI services increased by 7.1% annually. The percentage of all radiologists who were breast subspecialized increased from 8.1% in 2013 to 13.4% in 2022. BI wRVUs by subspecialized breast radiologists increased by 15.1% annually. Beginning in 2017, more BI wRVUs were performed by subspecialized breast radiologists than by <50% BI mix radiologists. The fraction of 2022 BI services provided by subspecialized breast radiologists varied by imaging modality: 63.7% for mammography, 70.0% for ultrasound, 80.5% for MRI, and 83.0% for interventional procedures.
Conclusions
The majority of BI examinations for US Medicare beneficiaries are now interpreted by subspecialized breast radiologists, potentially advancing quality of care and informing education, workforce, and access considerations.
{"title":"The Majority of Breast Imaging Services for US Medicare Beneficiaries Are Now Provided by Subspecialized Breast Radiologists","authors":"Tariq Rashid MD , Aditya Khurana MD , Alan Zhu BS , David Supeck DO, PhD , Laura Harper MD , Curtis Simmons MD, MBA , Richard E. Sharpe Jr. MD, MBA","doi":"10.1016/j.jacr.2025.10.003","DOIUrl":"10.1016/j.jacr.2025.10.003","url":null,"abstract":"<div><h3>Purpose</h3><div>The aim of this study was to evaluate breast imaging (BI) productivity trends in the US Medicare population from 2013 to 2022, including subspecialized interpretation trends overall, and by imaging modality.</div></div><div><h3>Methods</h3><div>Outpatient imaging claims for the US Medicare population from 2013 to 2022 were extracted from CMS databases, categorized as BI or not and by imaging modality. Total work relative value units (wRVUs) and wRVUs for BI studies were summed, and year-specific BI case mixes were calculated per radiologist. Radiologists interpreting more than 50% of their case mix as BI each year were classified as subspecialized breast radiologists. Productivity and subspecialization trends overall and by imaging modality were evaluated.</div></div><div><h3>Results</h3><div>From 2013 to 2022, BI wRVUs increased by 7.8% annually, and radiologists performing BI services decreased by 2.2% annually. The percentage of all radiologists submitting BI claims decreased from 46.5% to 34.2%. The number of subspecialized breast radiologists submitting claims for BI services increased by 7.1% annually. The percentage of all radiologists who were breast subspecialized increased from 8.1% in 2013 to 13.4% in 2022. BI wRVUs by subspecialized breast radiologists increased by 15.1% annually. Beginning in 2017, more BI wRVUs were performed by subspecialized breast radiologists than by <50% BI mix radiologists. The fraction of 2022 BI services provided by subspecialized breast radiologists varied by imaging modality: 63.7% for mammography, 70.0% for ultrasound, 80.5% for MRI, and 83.0% for interventional procedures.</div></div><div><h3>Conclusions</h3><div>The majority of BI examinations for US Medicare beneficiaries are now interpreted by subspecialized breast radiologists, potentially advancing quality of care and informing education, workforce, and access considerations.</div></div>","PeriodicalId":49044,"journal":{"name":"Journal of the American College of Radiology","volume":"23 1","pages":"Pages 36-42"},"PeriodicalIF":5.1,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145440164","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 : 2026-01-01DOI: 10.1016/j.jacr.2025.08.019
Susan C. Shelmerdine BSc, MBBS, MRCS, FRCR, PhD , Jaishree Naidoo MBChB, FCRad Diag, Dip Paed Rad , Brendan S. Kelly PhD, MB BCh, BAO, BSc, MSc, FFRRCSI , Lene Bjerke Laborie MD, PhD , Seema Toso MD, MSc, FMH , Tugba Akinci D’Antonoli MD , Owen J. Arthurs PhD, FRCR , Steven L. Blumer MD, MBA, CPE, FAAP , Pierluigi Ciet MD, PhD , Maria Beatrice Damasio , Andrea S. Doria MD, PhD, MSc, MBA , Saira Haque MBChB, MRCPCH, FRCR , Mai-Lan Ho MD , Thierry A.G.M. Huisman MD, PhD , Aparna Joshi MD , Jeevesh Kapur FRCR , Kshitij Mankad MRes, MRCP, PGDip in Health Care Mgmt, FRCR , Amaka C. Offiah BSc, MBBS, MRCP, FRCR, PhD , Hansel J. Otero MD , Erika Pace MD, MD(Res) , Marla Sammer MD, MHA
Artificial intelligence (AI) has potential to revolutionize radiology, yet current solutions and guidelines are predominantly focused on adult populations, often overlooking the specific requirements of children. This is important because children differ significantly from adults in terms of physiology, developmental stages, and clinical needs, necessitating tailored approaches for the safe and effective integration of AI tools. This multisociety position statement systematically addresses four critical pillars of AI adoption: (1) regulation and purchasing, (2) implementation and integration, (3) interpretation and postmarket surveillance, and (4) education. We propose pediatric-specific safety ratings, inclusion of datasets from diverse pediatric populations, quantifiable transparency metrics, and explainability of models to mitigate biases and ensure AI systems are appropriate for use in children. Risk assessment, dataset diversity, transparency, and cybersecurity are important steps in regulation and purchasing. For successful implementation, a phased strategy is recommended, involving early pilot testing, stakeholder engagement, and comprehensive postmarket surveillance with continuous monitoring of defined performance benchmarks. Clear protocols for managing discrepancies and adverse incident reporting are essential to maintain trust and safety. Moreover, we emphasize the need for foundational AI literacy courses for all health care professionals that include pediatric safety considerations, alongside specialized training for those directly involved in pediatric imaging. Public and patient engagement is crucial to foster understanding and acceptance of AI in pediatric radiology. Ultimately, we advocate for a child-centered framework for AI integration, ensuring that the distinct needs of children are prioritized and that their safety, accuracy, and overall well-being are safeguarded.
{"title":"Artificial Intelligence Implementation in Pediatric Radiology for Patient Safety: A Multisociety Statement From the ACR, ESPR, SPR, SLARP, AOSPR, SPIN","authors":"Susan C. Shelmerdine BSc, MBBS, MRCS, FRCR, PhD , Jaishree Naidoo MBChB, FCRad Diag, Dip Paed Rad , Brendan S. Kelly PhD, MB BCh, BAO, BSc, MSc, FFRRCSI , Lene Bjerke Laborie MD, PhD , Seema Toso MD, MSc, FMH , Tugba Akinci D’Antonoli MD , Owen J. Arthurs PhD, FRCR , Steven L. Blumer MD, MBA, CPE, FAAP , Pierluigi Ciet MD, PhD , Maria Beatrice Damasio , Andrea S. Doria MD, PhD, MSc, MBA , Saira Haque MBChB, MRCPCH, FRCR , Mai-Lan Ho MD , Thierry A.G.M. Huisman MD, PhD , Aparna Joshi MD , Jeevesh Kapur FRCR , Kshitij Mankad MRes, MRCP, PGDip in Health Care Mgmt, FRCR , Amaka C. Offiah BSc, MBBS, MRCP, FRCR, PhD , Hansel J. Otero MD , Erika Pace MD, MD(Res) , Marla Sammer MD, MHA","doi":"10.1016/j.jacr.2025.08.019","DOIUrl":"10.1016/j.jacr.2025.08.019","url":null,"abstract":"<div><div>Artificial intelligence (AI) has potential to revolutionize radiology, yet current solutions and guidelines are predominantly focused on adult populations, often overlooking the specific requirements of children. This is important because children differ significantly from adults in terms of physiology, developmental stages, and clinical needs, necessitating tailored approaches for the safe and effective integration of AI tools. This multisociety position statement systematically addresses four critical pillars of AI adoption: (1) regulation and purchasing, (2) implementation and integration, (3) interpretation and postmarket surveillance, and (4) education. We propose pediatric-specific safety ratings, inclusion of datasets from diverse pediatric populations, quantifiable transparency metrics, and explainability of models to mitigate biases and ensure AI systems are appropriate for use in children. Risk assessment, dataset diversity, transparency, and cybersecurity are important steps in regulation and purchasing. For successful implementation, a phased strategy is recommended, involving early pilot testing, stakeholder engagement, and comprehensive postmarket surveillance with continuous monitoring of defined performance benchmarks. Clear protocols for managing discrepancies and adverse incident reporting are essential to maintain trust and safety. Moreover, we emphasize the need for foundational AI literacy courses for all health care professionals that include pediatric safety considerations, alongside specialized training for those directly involved in pediatric imaging. Public and patient engagement is crucial to foster understanding and acceptance of AI in pediatric radiology. Ultimately, we advocate for a child-centered framework for AI integration, ensuring that the distinct needs of children are prioritized and that their safety, accuracy, and overall well-being are safeguarded.</div></div>","PeriodicalId":49044,"journal":{"name":"Journal of the American College of Radiology","volume":"23 1","pages":"Pages 89-101"},"PeriodicalIF":5.1,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145607798","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 : 2026-01-01DOI: 10.1016/j.jacr.2025.08.025
Alankrit Shatadal BS, BA , Allison Grayev MD
{"title":"Keeping Up With Communication: A Call to Involve Trainees","authors":"Alankrit Shatadal BS, BA , Allison Grayev MD","doi":"10.1016/j.jacr.2025.08.025","DOIUrl":"10.1016/j.jacr.2025.08.025","url":null,"abstract":"","PeriodicalId":49044,"journal":{"name":"Journal of the American College of Radiology","volume":"23 1","pages":"Pages 108-110"},"PeriodicalIF":5.1,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144981407","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 : 2026-01-01DOI: 10.1016/j.jacr.2025.08.049
Valeria Del Castillo MD, Laura Manuela Olarte Bermúdez MD, Valeria Noguera MD, Gustavo Triana MD, Hernan Dario Paez Rueda MD
{"title":"Radiologists as Equity Advocates: The Colombian Perspective","authors":"Valeria Del Castillo MD, Laura Manuela Olarte Bermúdez MD, Valeria Noguera MD, Gustavo Triana MD, Hernan Dario Paez Rueda MD","doi":"10.1016/j.jacr.2025.08.049","DOIUrl":"10.1016/j.jacr.2025.08.049","url":null,"abstract":"","PeriodicalId":49044,"journal":{"name":"Journal of the American College of Radiology","volume":"23 1","pages":"Pages 7-8"},"PeriodicalIF":5.1,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144994658","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 : 2026-01-01DOI: 10.1016/j.jacr.2025.09.013
Sean A. Woolen MD, MSc , Marisa Martin MD , Colby A. Foster MD , Mark P. MacEachern MLIS , Katherine E. Maturen MD, MSc
Objective
To summarize evidence for the environmental impact of radiology services and identify research gaps.
Methods
A scoping review was conducted following Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews guidelines. Searches were performed in Ovid Medline, Web of Science, Embase, and Scopus from inception to June 6, 2025. Studies were included if they reported environmental outcomes from diagnostic imaging or image-guided procedures in humans. Two reviewers independently screened studies and extracted data. Conference abstracts, narrative reviews, editorials, non-English articles, and studies without primary data were excluded. Data were charted by environmental impact type and summarized using descriptive statistics and narrative synthesis.
Results
Initial searches yielded 2,730 citations, with 115 studies included. Publications spanned 1971 to 2025, primarily from Europe (44%) and the United States (25%). Most were observational; only 8% (9 of 115) employed life cycle analysis (LCA). Key domains included energy use (27%), nuclear medicine waste (25%), and contrast media waste (14%). Reported annual CO2 emissions for equipment varied by modality: MRI (53.1 ± 13.2 metric tons [MT] of carbon dioxide equivalents), CT (12.6 ± 2.9 MT), interventional radiology (9.6 ± 1.0 MT), fluoroscopy (4.8 MT), radiography (0.7 ± 0.4 MT), workstations (0.7 ± 0.2 MT), and ultrasound (0.3 MT). Per-scan LCA estimates ranged widely: MRI (6.2-76.2 kg), CT (1.1-13.4 kg), ultrasound (0.1-1.2 kg), and radiography (0.7-7.0 kg). Radionuclides and contrast agents were frequently detected in wastewater and ecosystems. Key research gaps include inconsistent methods, limited LCA use, underexplored modalities and informatics, insufficient waste mitigation studies, and lack of cross-specialty carbon assessments.
Conclusion
Among thousands of publications on imaging sustainability, few provide primary data. This review consolidates evidence on radiology’s environmental impact and outlines priorities for future research.
目的：总结放射服务对环境影响的证据，并找出研究空白。方法：根据PRISMA-ScR指南进行范围审查。在Ovid Medline， Web of Science， Embase和Scopus中进行了从成立到2025年6月6日的搜索。如果研究报告了诊断成像或图像引导程序对人类的环境影响，则将其纳入研究。两位审稿人独立筛选研究并提取数据。会议摘要、叙述性综述、社论、非英语文章和没有原始数据的研究被排除在外。数据按环境影响类型绘制图表，并使用描述性统计和叙事综合进行汇总。结果：最初的搜索产生了2730条引用，其中包括115项研究。论文发表时间从1971年到2025年，主要来自欧洲（44%）和美国（25%）。大多数是观察性的；只有8%（9/115）采用了生命周期分析（LCA）。关键领域包括能源使用（27%）、核医学废物（25%）和造影剂废物（14%）。报告的设备年二氧化碳排放量因方式而异：MRI（53.1±13.2 MT）、CT（12.6±2.9 MT）、IR（9.6±1.0 MT）、透视（4.8 MT）、x线摄影（0.7±0.4 MT）、工作站（0.7±0.2 MT）和超声（0.3 MT）。每次扫描的LCA估计范围很广：MRI (6.2-76.2 kg), CT (1.1-13.4 kg)，超声（0.1-1.2 kg）和x线摄影（0.7-7.0 kg）。在废水和生态系统中经常检测到放射性核素和造影剂。主要的研究差距包括方法不一致、LCA使用有限、模式和信息学探索不足、废物缓解研究不足以及缺乏跨专业碳评估。结论：在数以千计的关于成像可持续性的出版物中，很少提供原始数据。这篇综述巩固了放射学对环境影响的证据，并概述了未来研究的重点。
{"title":"Green Imaging: Scoping Review of Radiology’s Environmental Impact","authors":"Sean A. Woolen MD, MSc , Marisa Martin MD , Colby A. Foster MD , Mark P. MacEachern MLIS , Katherine E. Maturen MD, MSc","doi":"10.1016/j.jacr.2025.09.013","DOIUrl":"10.1016/j.jacr.2025.09.013","url":null,"abstract":"<div><h3>Objective</h3><div>To summarize evidence for the environmental impact of radiology services and identify research gaps.</div></div><div><h3>Methods</h3><div>A scoping review was conducted following Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews guidelines. Searches were performed in Ovid Medline, Web of Science, Embase, and Scopus from inception to June 6, 2025. Studies were included if they reported environmental outcomes from diagnostic imaging or image-guided procedures in humans. Two reviewers independently screened studies and extracted data. Conference abstracts, narrative reviews, editorials, non-English articles, and studies without primary data were excluded. Data were charted by environmental impact type and summarized using descriptive statistics and narrative synthesis.</div></div><div><h3>Results</h3><div>Initial searches yielded 2,730 citations, with 115 studies included. Publications spanned 1971 to 2025, primarily from Europe (44%) and the United States (25%). Most were observational; only 8% (9 of 115) employed life cycle analysis (LCA). Key domains included energy use (27%), nuclear medicine waste (25%), and contrast media waste (14%). Reported annual CO<sub>2</sub> emissions for equipment varied by modality: MRI (53.1 ± 13.2 metric tons [MT] of carbon dioxide equivalents), CT (12.6 ± 2.9 MT), interventional radiology (9.6 ± 1.0 MT), fluoroscopy (4.8 MT), radiography (0.7 ± 0.4 MT), workstations (0.7 ± 0.2 MT), and ultrasound (0.3 MT). Per-scan LCA estimates ranged widely: MRI (6.2-76.2 kg), CT (1.1-13.4 kg), ultrasound (0.1-1.2 kg), and radiography (0.7-7.0 kg). Radionuclides and contrast agents were frequently detected in wastewater and ecosystems. Key research gaps include inconsistent methods, limited LCA use, underexplored modalities and informatics, insufficient waste mitigation studies, and lack of cross-specialty carbon assessments.</div></div><div><h3>Conclusion</h3><div>Among thousands of publications on imaging sustainability, few provide primary data. This review consolidates evidence on radiology’s environmental impact and outlines priorities for future research.</div></div>","PeriodicalId":49044,"journal":{"name":"Journal of the American College of Radiology","volume":"23 1","pages":"Pages 123-135"},"PeriodicalIF":5.1,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145103082","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 : 2026-01-01DOI: 10.1016/j.jacr.2025.06.027
Priscilla J. Slanetz MD, MPH , Anastacia Wahl MS , James C. Anderson MD , Anna Rozenshtein MD, MPH
{"title":"Radiology Residency Recruitment: The Perils and Promises of Seeking a “Fit”","authors":"Priscilla J. Slanetz MD, MPH , Anastacia Wahl MS , James C. Anderson MD , Anna Rozenshtein MD, MPH","doi":"10.1016/j.jacr.2025.06.027","DOIUrl":"10.1016/j.jacr.2025.06.027","url":null,"abstract":"","PeriodicalId":49044,"journal":{"name":"Journal of the American College of Radiology","volume":"23 1","pages":"Pages 114-116"},"PeriodicalIF":5.1,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144340768","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 : 2026-01-01DOI: 10.1016/j.jacr.2025.08.003
Douglas Spaeth-Cook DO , Morgan P. McBee MD, CIIP , Margaret C. Lin MD , Peter D. Chang MD , Elias G. Kikano MD
{"title":"JACR Expert Panel: Artificial Intelligence in Radiology Residency Training","authors":"Douglas Spaeth-Cook DO , Morgan P. McBee MD, CIIP , Margaret C. Lin MD , Peter D. Chang MD , Elias G. Kikano MD","doi":"10.1016/j.jacr.2025.08.003","DOIUrl":"10.1016/j.jacr.2025.08.003","url":null,"abstract":"","PeriodicalId":49044,"journal":{"name":"Journal of the American College of Radiology","volume":"23 1","pages":"Pages 111-113"},"PeriodicalIF":5.1,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144849973","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 : 2026-01-01DOI: 10.1016/j.jacr.2025.11.016
Matthew D. Phelps MD, Diana L. Lam MD
{"title":"Nationwide Trends Show Substantial Shift Toward Breast Imaging Subspecialization","authors":"Matthew D. Phelps MD, Diana L. Lam MD","doi":"10.1016/j.jacr.2025.11.016","DOIUrl":"10.1016/j.jacr.2025.11.016","url":null,"abstract":"","PeriodicalId":49044,"journal":{"name":"Journal of the American College of Radiology","volume":"23 1","pages":"Pages 43-44"},"PeriodicalIF":5.1,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145530798","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}
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