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

Patient-Friendly Summary of the ACR Appropriateness Criteria®: Imaging of Ductal Carcinoma in Situ (DCIS). 对患者友好的ACR适宜性标准总结：导管原位癌（DCIS）成像。

Journal of the American College of Radiology : JACR Pub Date : 2025-05-09 DOI: 10.1016/j.jacr.2025.05.007

Sania Choudhary, Sonya Bhole

引用次数: 0

Patient-Friendly Summary of the ACR Appropriateness Criteria®: Imaging for Pulmonary Embolism, Known Clot. ACR适宜性标准总结：肺栓塞、已知血栓的影像学检查。

Journal of the American College of Radiology : JACR Pub Date : 2025-05-09 DOI: 10.1016/j.jacr.2025.05.006

Veer S Sawhney, Sharon D'Souza

引用次数: 0

Advancing Healthcare Delivery in Community, Rural, and Safety Net Settings. 在社区、农村和安全网环境中推进医疗保健服务。

Journal of the American College of Radiology : JACR Pub Date : 2025-05-06 DOI: 10.1016/j.jacr.2025.05.003

Randy C Miles, Anand Narayan

引用次数: 0

Opinions and preferences regarding artificial intelligence use in healthcare delivery: results from a national multi-site survey of breast imaging patients. 关于在医疗保健服务中使用人工智能的意见和偏好：来自乳腺成像患者的全国多地点调查的结果。

Journal of the American College of Radiology : JACR Pub Date : 2025-05-06 DOI: 10.1016/j.jacr.2025.05.001

Brian N Dontchos, Katerina Dodelzon, Sonya Bhole, Christine E Edmonds, Lisa A Mullen, Jay R Parikh, Carolyn P Daly, James A Epling, Soren Christensen, Lars J Grimm

{"title":"Opinions and preferences regarding artificial intelligence use in healthcare delivery: results from a national multi-site survey of breast imaging patients.","authors":"Brian N Dontchos, Katerina Dodelzon, Sonya Bhole, Christine E Edmonds, Lisa A Mullen, Jay R Parikh, Carolyn P Daly, James A Epling, Soren Christensen, Lars J Grimm","doi":"10.1016/j.jacr.2025.05.001","DOIUrl":"https://doi.org/10.1016/j.jacr.2025.05.001","url":null,"abstract":"Objective: Artificial intelligence (AI) utilization is growing, but patient perceptions of AI are unclear. Our objective was to understand patient perceptions of AI through a multi-site survey of breast imaging patients.Methods: A 36-question survey was distributed to eight US practices (6 academic, 2 non-academic) from October 2023 through October 2024. This manuscript analyzes a subset of questions from the survey addressing digital health literacy and attitudes towards AI in medicine and breast imaging specifically. Multivariable analysis compared responses by respondent demographics.Results: A total of 3,532 surveys were collected (response rate: 69.9%, 3,532/5053). Median respondent age was 55 years (IQR 20). Most respondents were White (73.0%, 2579/3532) and had completed college (77.3%, 2732/3532). Overall, respondents were undecided (range: 43.2%-50.8%) regarding questions about general perceptions of AI in healthcare. Respondents with higher electronic health literacy, more education, and younger age were significantly more likely to consider it useful to use utilize AI for aiding medical tasks (all p<0.001). In contrast, respondents with lower electronic health literacy and less education were significantly more likely to indicate it was a bad idea for AI to perform medical tasks (p<0.001). Non-White patients were more likely to express concerns that AI will not work as well for some groups compared to others (p<0.05). Overall, favorable opinions of AI use for medical tasks were associated with younger age, more education, and higher electronic health literacy.Discussion: As AI is increasingly implemented into clinical workflows, it is important to educate patients and provide transparency to build patient understanding and trust.","PeriodicalId":73968,"journal":{"name":"Journal of the American College of Radiology : JACR","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144030154","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}

引用次数: 0

Cybersecurity Strategies for Radiology Leaders: A Private Practice Perspective. 放射学领导者的网络安全策略：私人实践视角。

Journal of the American College of Radiology : JACR Pub Date : 2025-05-06 DOI: 10.1016/j.jacr.2025.05.004

Valeria Makeeva, Matt Long

引用次数: 0

Patient-Friendly Summary of the ACR Appropriateness Criteria®: Imaging of Suspected Intracranial Hypotension. ACR适宜性标准总结：疑似颅内低血压的影像学检查。

Journal of the American College of Radiology : JACR Pub Date : 2025-05-06 DOI: 10.1016/j.jacr.2025.05.005

Corey Feuer, Luke Ledbetter

引用次数: 0

When Confidence Overpowers Competence: The Dunning-Kruger Effect in Radiology. 当信心压倒能力：放射学中的邓宁-克鲁格效应。

Journal of the American College of Radiology : JACR Pub Date : 2025-05-06 DOI: 10.1016/j.jacr.2025.05.002

Subha Ghosh

引用次数: 0

Hospital Price Transparency in Action: An Analysis of Radiology Procedure Price Variation in Children's Hospitals Between 2023 and 2024. 医院价格透明的作用：2023 - 2024年儿童医院放射手术价格变化分析

Journal of the American College of Radiology : JACR Pub Date : 2025-05-02 DOI: 10.1016/j.jacr.2025.04.037

Philip Krotenok, Summer Kaplan

{"title":"Hospital Price Transparency in Action: An Analysis of Radiology Procedure Price Variation in Children's Hospitals Between 2023 and 2024.","authors":"Philip Krotenok, Summer Kaplan","doi":"10.1016/j.jacr.2025.04.037","DOIUrl":"https://doi.org/10.1016/j.jacr.2025.04.037","url":null,"abstract":"The Federal Hospital Price Transparency Final Rule enables parties such as patients, hospitals, and payers to view comprehensive pricing for U.S. hospital services, which encourages both patient financial decision making and market competition to potentially affect future hospital pricing. This study examines price levels and price variation for three common outpatient imaging procedures-MRI brain with and without contrast, radiograph chest (2-view), and nuclear medicine gastric emptying-across 26 U.S. children's hospitals in 2023 and 2024. Data from machine readable files from hospital websites such as gross charges, cash prices, and payer specific negotiated rates for five major commercial insurers were collected to analyze trends. Findings show a statistically significant decrease in price variation, with the coefficient of variation (CV) for negotiated rates declining by 19.2% but overall prices continuing to rise, with payer specific negotiated rates increasing by an average 6.7%. Price transparency policies may contribute to reduced price dispersion; however, the average price did increase.","PeriodicalId":73968,"journal":{"name":"Journal of the American College of Radiology : JACR","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144065317","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}

引用次数: 0

Communication Errors in Radiology: A Single Center Experience and Development of a Classification System. 放射学中的沟通错误：单一中心的经验和分类系统的发展。

Journal of the American College of Radiology : JACR Pub Date : 2025-05-02 DOI: 10.1016/j.jacr.2025.04.030

Meghavi Mashar, Suzanne Swedeen, Olga R Brook, Jennifer Cutts, Bettina Siewert

引用次数: 0

Large Language Models for Global Health Clinics: Opportunities and Challenges. 全球卫生诊所的大型语言模型：机遇与挑战。

Journal of the American College of Radiology : JACR Pub Date : 2025-04-29 DOI: 10.1016/j.jacr.2025.04.007

Satvik Tripathi, Dana Alkhulaifat, Meghana Muppuri, Ameena Elahi, Farouk Dako

{"title":"Large Language Models for Global Health Clinics: Opportunities and Challenges.","authors":"Satvik Tripathi, Dana Alkhulaifat, Meghana Muppuri, Ameena Elahi, Farouk Dako","doi":"10.1016/j.jacr.2025.04.007","DOIUrl":"https://doi.org/10.1016/j.jacr.2025.04.007","url":null,"abstract":"Large language models (LLMs) have emerged as a new wave of artificial intelligence, and their applications could emerge as a pivotal resource capable of reshaping health care communication, research, and informed decision-making processes. These models offer unprecedented potential to swiftly disseminate critical health information and transcend linguistic barriers. However, their integration into health care systems presents formidable challenges, including inherent biases in training data, privacy vulnerabilities, and disparities in digital literacy. Despite these obstacles, LLMs possess unparalleled analytic prowess to inform evidence-based health care policies and clinical practices. Addressing these challenges necessitates the formulation of robust ethical frameworks, bias mitigation strategies, and educational initiatives to ensure equitable access to health care resources globally. By navigating these complexities with meticulous attention and foresight, LLMs stand poised to catalyze substantial advancements in global health outcomes, promoting health equity and improving population health worldwide.","PeriodicalId":73968,"journal":{"name":"Journal of the American College of Radiology : JACR","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144058129","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}

引用次数: 0