Journal of the American College of Radiology最新文献

英文中文

Issues Raised by a Decision Support Tool That Decreases Utilization Without Increasing Yield 决策支持工具引起的问题降低了利用率，但没有增加产量。

IF 5.1 3区医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

Journal of the American College of Radiology

Pub Date : 2026-01-01 DOI: 10.1016/j.jacr.2025.02.048

Guy W. Soo Hoo MD

引用次数: 0

Impact of Artificial Intelligence Triage on Radiologist Report Turnaround Time: Real-World Time Savings and Insights From Model Predictions 人工智能分类对放射科医生报告周转时间的影响：现实世界的时间节省和模型预测的见解。

IF 5.1 3区医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

Journal of the American College of Radiology

Pub Date : 2026-01-01 DOI: 10.1016/j.jacr.2025.07.033

Yee Lam Elim Thompson PhD , Jonathan Fergus MD , Jonathan Chung MD , Jana G. Delfino PhD , Weijie Chen PhD , Gary M. Levine MD , Frank W. Samuelson PhD

Objective

To quantify the impact of workflow parameters on time savings in report turnaround time due to an AI triage device that prioritized pulmonary embolism (PE) in chest CT pulmonary angiography (CTPA) examinations.

Methods

This retrospective study analyzed 11,252 adult CTPA examinations conducted for suspected PE at a single tertiary academic medical center. Data was divided into two periods: pre-artificial intelligence (AI) and post-AI. For PE-positive examinations, turnaround time (TAT)—defined as the duration from patient scan completion to the first preliminary report completion—was compared between the two periods. Time savings were reported separately for work-hour and off-hour cohorts. To characterize radiologist workflow, 527,234 records were retrieved from the PACS and workflow parameters such as examination interarrival time and radiologist read time extracted. These parameters were input into a computational model to predict time savings after deployment of an AI triage device and to study the impact of workflow parameters.

Results

The pre-AI dataset included 4,694 chest CTPA examinations with 13.3% being PE-positive. The post-AI dataset comprised 6,558 examinations with 16.2% being PE-positive. The mean TAT for pre-AI and post-AI during work hours are 68.9 (95% confidence interval 55.0-82.8) and 46.7 (38.1-55.2) min, respectively, and those during off-hours are 44.8 (33.7-55.9) and 42.0 (33.6-50.3) min. Clinically observed time savings during work hours (22.2 [95% confidence interval: 5.85-38.6] min) were significant (P = .004), while off-hour (2.82 [−11.1 to 16.7] min) were not (P = .345). Observed time savings aligned with model predictions (29.6 [95% range: 23.2-38.1] min for work hours; 2.10 [1.76, 2.58] min for off-hours).

Discussion

Consideration and quantification of the clinical workflow contributes to the accurate assessment of the expected time savings in report TAT after deployment of an AI triage device.

目的：量化人工智能分诊设备在胸部CT肺血管造影（CTPA）检查中优先考虑肺栓塞（PE）后，工作流程参数对报告周转时间节省的影响。方法：本回顾性研究分析了在单一三级学术医疗中心进行的11,252例疑似PE的成人CTPA检查。数据分为前人工智能（AI）和后人工智能两个阶段。对于pe阳性检查，周转时间(TAT)-定义为从患者扫描完成到首次初步报告完成的持续时间-在两个时间段之间进行比较。节省的时间分别报告了工作时间组和非工作时间组。为了描述放射科医生的工作流程，从PACS中检索了527,234条记录，并提取了诸如检查间隔时间和放射科医生阅读时间等工作流程参数。这些参数被输入到计算模型中，以预测部署人工智能分诊设备后节省的时间，并研究工作流程参数的影响。结果：ai前数据集包括4,694例胸部CTPA检查，其中13.3%为pe阳性。后ai数据集包括6558次检查，其中16.2%为pe阳性。人工智能前和人工智能后的平均TAT在工作时间分别为68.9（95%可信区间为55.0-82.8）和46.7 (38.1-55.2)min，非工作时间分别为44.8（33.7-55.9）和42.0 (33.6-50.3)min。临床观察工作时间节约时间（22.2[95%可信区间：5.85-38.6]min）有统计学意义（P = 0.004），非工作时间节约时间（2.82 [-11.1 ~ 16.7]min）无统计学意义（P = .345）。观察到的时间节省与模型预测一致（工作时间为29.6[95%范围：23.2-38.1]分钟；非工作时间为2.10[1.76,2.58]分钟）。讨论：临床工作流程的考虑和量化有助于准确评估部署人工智能分诊设备后报告TAT预期节省的时间。

{"title":"Impact of Artificial Intelligence Triage on Radiologist Report Turnaround Time: Real-World Time Savings and Insights From Model Predictions","authors":"Yee Lam Elim Thompson PhD , Jonathan Fergus MD , Jonathan Chung MD , Jana G. Delfino PhD , Weijie Chen PhD , Gary M. Levine MD , Frank W. Samuelson PhD","doi":"10.1016/j.jacr.2025.07.033","DOIUrl":"10.1016/j.jacr.2025.07.033","url":null,"abstract":"<div><h3>Objective</h3><div>To quantify the impact of workflow parameters on time savings in report turnaround time due to an AI triage device that prioritized pulmonary embolism (PE) in chest CT pulmonary angiography (CTPA) examinations.</div></div><div><h3>Methods</h3><div>This retrospective study analyzed 11,252 adult CTPA examinations conducted for suspected PE at a single tertiary academic medical center. Data was divided into two periods: pre-artificial intelligence (AI) and post-AI. For PE-positive examinations, turnaround time (TAT)—defined as the duration from patient scan completion to the first preliminary report completion—was compared between the two periods. Time savings were reported separately for work-hour and off-hour cohorts. To characterize radiologist workflow, 527,234 records were retrieved from the PACS and workflow parameters such as examination interarrival time and radiologist read time extracted. These parameters were input into a computational model to predict time savings after deployment of an AI triage device and to study the impact of workflow parameters.</div></div><div><h3>Results</h3><div>The pre-AI dataset included 4,694 chest CTPA examinations with 13.3% being PE-positive. The post-AI dataset comprised 6,558 examinations with 16.2% being PE-positive. The mean TAT for pre-AI and post-AI during work hours are 68.9 (95% confidence interval 55.0-82.8) and 46.7 (38.1-55.2) min, respectively, and those during off-hours are 44.8 (33.7-55.9) and 42.0 (33.6-50.3) min. Clinically observed time savings during work hours (22.2 [95% confidence interval: 5.85-38.6] min) were significant (<em>P</em> = .004), while off-hour (2.82 [−11.1 to 16.7] min) were not (<em>P</em> = .345). Observed time savings aligned with model predictions (29.6 [95% range: 23.2-38.1] min for work hours; 2.10 [1.76, 2.58] min for off-hours).</div></div><div><h3>Discussion</h3><div>Consideration and quantification of the clinical workflow contributes to the accurate assessment of the expected time savings in report TAT after deployment of an AI triage device.</div></div>","PeriodicalId":49044,"journal":{"name":"Journal of the American College of Radiology","volume":"23 1","pages":"Pages 63-73"},"PeriodicalIF":5.1,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145202239","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}

引用次数: 0

Association Between Practice Closure and Subsequent Subspecialization Among Affiliated Radiologists 附属放射科医师执业结束与后续专科分工的关系。

IF 5.1 3区医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

Journal of the American College of Radiology

Pub Date : 2026-01-01 DOI: 10.1016/j.jacr.2025.08.014

Eric W. Christensen PhD , Chi-Mei Liu PhD , Jay R. Parikh MD , YoonKyung Chung PhD , Elizabeth Y. Rula PhD

Purpose

To examine the association of practice closure with the likelihood that individual radiologists will subsequently practice as subspecialists given continued subspecialization and closure trends.

Methods

CMS data (2014-2022) were used to determine whether individual radiologists practiced as subspecialists and whether they were affiliated with a practice that closed. Radiologists with >50% of their workload in a single subspecialty were classified as subspecialists versus generalists. Differences between radiologists with and without a practice closure were assessed with χ² tests; unadjusted subspecialization rates were assessed by practice type, size, and urbanicity. Multivariable logistic regression was used to estimate whether radiologists who experienced a practice closure were more or less likely to practice as a subspecialist in the following year, controlling for radiologist and practice characteristics.

Results

The study included 238,725 radiologist years. The year-over-year unadjusted increase in the share of radiologists practicing as subspecialists was 2.5 percentage points after a practice closure compared with 1.8 percentage points for those whose practice did not close. Adjusted for radiologist and practice characteristics, radiologists with a closure had 10% higher odds of practicing as a subspecialist in the following year (odds ratio, 1.10; 95% confidence interval, 1.03-1.17). More years of practice, larger practices, and multispecialty practices were significantly associated with increased subspecialization after a closure.

Conclusion

Radiologists who experienced practice closure were more likely to practice as subspecialists afterward. This increased likelihood is distinct from the overall temporal trend of increasing subspecialization among radiologists. Hence, practice closure may be associated with increased subspecialization beyond the general trend.

目的：研究实践关闭与个体放射科医生随后作为亚专科医生执业的可能性之间的关系，因为持续的亚专科和关闭趋势。方法：使用CMS数据（2014-2022年）来确定个别放射科医生是否作为亚专科医生执业，以及他们是否隶属于已关闭的诊所。放射科医生在单个专科的工作量占其总工作量的50%，被归类为亚专科医生与全科医生。采用χ2检验评估有和没有执业关闭的放射科医师之间的差异；未调整的次专业化比率按实践类型、规模和城市化程度进行评估。使用多变量逻辑回归来估计经历执业关闭的放射科医生在下一年是否更有可能作为亚专科医生执业，控制放射科医生和执业特征。结果：该研究包括238,725放射科医师年。在诊所关闭后，作为亚专科执业的放射科医生的比例同比未经调整的增长为2.5个百分点，而那些诊所未关闭的放射科医生的比例为1.8个百分点。根据放射科医生和执业特征调整后，关闭放射科医生在接下来的一年里作为专科医生执业的几率高出10%（优势比为1.10；95%可信区间为1.03-1.17）。更多年的实践，更大的实践和多专业实践与关闭后增加的亚专业化显著相关。结论：经历过实习关闭的放射科医师更有可能在实习结束后成为专科医师。这种增加的可能性与放射科医生不断增加的亚专业化的总体时间趋势不同。因此，实践封闭可能与超出一般趋势的增加的子专门化相关联。

{"title":"Association Between Practice Closure and Subsequent Subspecialization Among Affiliated Radiologists","authors":"Eric W. Christensen PhD , Chi-Mei Liu PhD , Jay R. Parikh MD , YoonKyung Chung PhD , Elizabeth Y. Rula PhD","doi":"10.1016/j.jacr.2025.08.014","DOIUrl":"10.1016/j.jacr.2025.08.014","url":null,"abstract":"<div><h3>Purpose</h3><div>To examine the association of practice closure with the likelihood that individual radiologists will subsequently practice as subspecialists given continued subspecialization and closure trends.</div></div><div><h3>Methods</h3><div>CMS data (2014-2022) were used to determine whether individual radiologists practiced as subspecialists and whether they were affiliated with a practice that closed. Radiologists with >50% of their workload in a single subspecialty were classified as subspecialists versus generalists. Differences between radiologists with and without a practice closure were assessed with χ<sup>2</sup> tests; unadjusted subspecialization rates were assessed by practice type, size, and urbanicity. Multivariable logistic regression was used to estimate whether radiologists who experienced a practice closure were more or less likely to practice as a subspecialist in the following year, controlling for radiologist and practice characteristics.</div></div><div><h3>Results</h3><div>The study included 238,725 radiologist years. The year-over-year unadjusted increase in the share of radiologists practicing as subspecialists was 2.5 percentage points after a practice closure compared with 1.8 percentage points for those whose practice did not close. Adjusted for radiologist and practice characteristics, radiologists with a closure had 10% higher odds of practicing as a subspecialist in the following year (odds ratio, 1.10; 95% confidence interval, 1.03-1.17). More years of practice, larger practices, and multispecialty practices were significantly associated with increased subspecialization after a closure.</div></div><div><h3>Conclusion</h3><div>Radiologists who experienced practice closure were more likely to practice as subspecialists afterward. This increased likelihood is distinct from the overall temporal trend of increasing subspecialization among radiologists. Hence, practice closure may be associated with increased subspecialization beyond the general trend.</div></div>","PeriodicalId":49044,"journal":{"name":"Journal of the American College of Radiology","volume":"23 1","pages":"Pages 27-35"},"PeriodicalIF":5.1,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145379735","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}

引用次数: 0

Data, Risk, Adaptation, and Resilience in Modern Health Care 现代医疗保健中的数据、风险、适应和弹性。

IF 5.1 3区医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

Journal of the American College of Radiology

Pub Date : 2026-01-01 DOI: 10.1016/j.jacr.2025.06.028

Ted Giovanis , Elliot K. Fishman MD , Linda C. Chu MD , Steven P. Rowe MD, PhD , Charles K. Crawford BS

引用次数: 0

Toward Pediatric Patient–Friendly Education Material Using Generative Artificial Intelligence 利用生成式人工智能制作儿科患者友好型教育材料。

IF 5.1 3区医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

Journal of the American College of Radiology

Pub Date : 2026-01-01 DOI: 10.1016/j.jacr.2025.07.009

Satvik Tripathi , Dana Alkhulaifat MD , Hansel J. Otero MD , Tessa S. Cook MD, PhD

引用次数: 0

Patient-Friendly Summary of the ACR Appropriateness Criteria®: Chronic Knee Pain 对患者友好的ACR适宜性标准总结：慢性膝关节疼痛。

IF 5.1 3区医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

Journal of the American College of Radiology

Pub Date : 2026-01-01 DOI: 10.1016/j.jacr.2025.08.022

Sania Choudhary , Shari T. Jawetz MD

引用次数: 0

Can Artificial Intelligence Cure Baumol’s Cost Disease? 人工智能能治愈鲍莫尔成本病吗？

IF 5.1 3区医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

Journal of the American College of Radiology

Pub Date : 2026-01-01 DOI: 10.1016/j.jacr.2025.08.023

Amy L. Kotsenas MD , Ajay Malhotra MD, MMM , David Seidenwurm MD

引用次数: 0

Patient-Friendly Summary of the ACR Appropriateness Criteria®: Breast Imaging During Pregnancy 对患者友好的ACR适宜性标准总结®：妊娠期间的乳房成像。

IF 5.1 3区医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

Journal of the American College of Radiology

Pub Date : 2026-01-01 DOI: 10.1016/j.jacr.2025.10.015

Yash Sachin Saboo , Sonya Bhole MD

引用次数: 0

A New Chapter for the ACR Appropriateness Criteria® ACR适当性标准的新篇章

IF 5.1 3区医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

Journal of the American College of Radiology

Pub Date : 2026-01-01 DOI: 10.1016/j.jacr.2025.11.027

Ihab R. Kamel MD, PhD

引用次数: 0

Table of Content 目录表

IF 5.1 3区医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

Journal of the American College of Radiology

Pub Date : 2026-01-01 DOI: 10.1016/S1546-1440(25)00644-1

引用次数: 0

首页上一页

下一页尾页

类型

全部化学•材料生命科学医学物理工程技术环境•农林材料科学地球科学法学管理学化学环境科学与生态学计算机科学教育学经济学农林科学人文科学生物学数学物理与天体物理心理学综合性期刊其他工业工程理学历史学农学文学信息工程

数据库

全部 ACS Publications Elsevier ieeexplore Springer The Royal Society of Chemistry Wiley

期刊

Journal of the American College of Radiology

全部 Acc. Chem. Res. ACS Applied Bio Materials ACS Appl. Electron. Mater. ACS Appl. Energy Mater. ACS Appl. Mater. Interfaces ACS Appl. Nano Mater. ACS Appl. Polym. Mater. ACS BIOMATER-SCI ENG ACS Catal. ACS Cent. Sci. ACS Chem. Biol. ACS Chemical Health & Safety ACS Chem. Neurosci. ACS Comb. Sci. ACS Earth Space Chem. ACS Energy Lett. ACS Infect. Dis. ACS Macro Lett. ACS Mater. Lett. ACS Med. Chem. Lett. ACS Nano ACS Omega ACS Photonics ACS Sens. ACS Sustainable Chem. Eng. ACS Synth. Biol. Anal. Chem. BIOCHEMISTRY-US Bioconjugate Chem. BIOMACROMOLECULES Chem. Res. Toxicol. Chem. Rev. Chem. Mater. CRYST GROWTH DES ENERG FUEL Environ. Sci. Technol. Environ. Sci. Technol. Lett. Eur. J. Inorg. Chem. IND ENG CHEM RES Inorg. Chem. J. Agric. Food. Chem. J. Chem. Eng. Data J. Chem. Educ. J. Chem. Inf. Model. J. Chem. Theory Comput. J. Med. Chem. J. Nat. Prod. J PROTEOME RES J. Am. Chem. Soc. LANGMUIR MACROMOLECULES Mol. Pharmaceutics Nano Lett. Org. Lett. ORG PROCESS RES DEV ORGANOMETALLICS J. Org. Chem. J. Phys. Chem. J. Phys. Chem. A J. Phys. Chem. B J. Phys. Chem. C J. Phys. Chem. Lett. Analyst Anal. Methods Biomater. Sci. Catal. Sci. Technol. Chem. Commun. Chem. Soc. Rev. CHEM EDUC RES PRACT CRYSTENGCOMM Dalton Trans. Energy Environ. Sci. ENVIRON SCI-NANO ENVIRON SCI-PROC IMP ENVIRON SCI-WAT RES Faraday Discuss. Food Funct. Green Chem. Inorg. Chem. Front. Integr. Biol. J. Anal. At. Spectrom. J. Mater. Chem. A J. Mater. Chem. B J. Mater. Chem. C Lab Chip Mater. Chem. Front. Mater. Horiz. MEDCHEMCOMM Metallomics Mol. Biosyst. Mol. Syst. Des. Eng. Nanoscale Nanoscale Horiz. Nat. Prod. Rep. New J. Chem. Org. Biomol. Chem. Org. Chem. Front. PHOTOCH PHOTOBIO SCI PCCP Polym. Chem.

﹀