Journal of the American College of Radiology最新文献

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Evolving With Artificial Intelligence: Integrating Artificial Intelligence and Imaging Informatics in a General Residency Curriculum With an Advanced Track 与人工智能共同发展：将人工智能和成像信息学纳入普通住院医师课程，并开设高级课程。

IF 4 3区医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

Journal of the American College of Radiology

Pub Date : 2024-10-01 DOI: 10.1016/j.jacr.2024.07.007

Ali S. Tejani MD , Ronald M. Peshock MD , Karuna M. Raj MD

引用次数: 0

Building a Career in Radiology Innovation: A Primer for Trainees and First-Time Innovators to Act on Opportunities 建立放射学创新事业：受训者和首次创新者抓住机遇的入门指南。

IF 4 3区医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

Journal of the American College of Radiology

Pub Date : 2024-10-01 DOI: 10.1016/j.jacr.2024.06.010

引用次数: 0

Building Bridges: Future-Proofing Established Industries and Building Relationships with the Black Community 搭建桥梁：经得起未来考验的既定产业和与黑人社区建立关系。

IF 4 3区医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

Journal of the American College of Radiology

Pub Date : 2024-10-01 DOI: 10.1016/j.jacr.2023.08.039

引用次数: 0

Medical School House Rock: Randomized Trial 医学院宿舍摇滚随机试验

IF 4 3区医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

Journal of the American College of Radiology

Pub Date : 2024-10-01 DOI: 10.1016/j.jacr.2024.04.001

引用次数: 0

Analysis of National Resident Matching Program for Radiology Fellowships: Factors Affecting Program Fill Rates 全国放射学研究员驻地配对计划分析--影响计划填补率的因素。

IF 4 3区医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

Journal of the American College of Radiology

Pub Date : 2024-10-01 DOI: 10.1016/j.jacr.2024.04.011

Purpose

The National Resident Matching Program (NRMP) is used by an increasing number of diagnostic radiology (DR) residents applying to subspecialty fellowships. Data characterizing match outcomes on the basis of program characteristics are limited. The aim of this study was to determine if fellowship or residency size, location, or perceived reputation was related with a program filling its quota.

Methods

Using public NRMP data from 2004 to 2022, DR residency, breast imaging (BI), musculoskeletal imaging (MSK), interventional radiology (IR), and neuroradiology (NR) fellowship programs were characterized by geography, DR and fellowship quota, applicants per position (A/P), and reputation as determined by being an Aunt Minnie best DR program semifinalist, Doximity 2021-2022 top 25 program, or U.S. News & World Report top 20 hospital. The DR program’s reputation was substituted for fellowships at the same institution. A program was considered filled if it met its quota.

Results

The 2022 A/P ratios were 1.02 for IR, 0.83 for BI, 0.75 for MSK, and 0.88 for NR. IR was excluded from additional analysis because its A/P was >1. The combined BI, MSK, and NR fellowships filled 78% of positions (529 of 679) and 56% of programs (132 of 234). Factors associated with higher program filling included Doximity top 25 program, Aunt Minnie semifinalist, and U.S. News & World Report top 20 hospital affiliation (P < .001 for all); DR residency quota greater than 9, and fellowship quota of three or more (P < .01). The Ohio Valley (Ohio, western Pennsylvania, West Virginia, and Kentucky) filled the lowest, at 39% of programs (P = .06).

Conclusions

Larger fellowship programs with higher perceived reputations and larger underlying DR residency programs were significantly more likely to fill their NRMP quota.

目的：越来越多的放射诊断学（DR）住院医师在申请亚专科研究金时使用了国家住院医师匹配计划（NRMP）。根据项目特征描述匹配结果的数据非常有限。我们试图确定研究员或住院医师的规模、地点或声誉是否与项目是否完成配额有关：我们利用 2004-2022 年的 NRMP 公开数据，按照地理位置、DR 和研究金配额、每个职位的申请人数（A/P）以及声誉（由 "米妮阿姨最佳 DR 项目半决赛"、"Doximity 2021-2022 年 25 强 "或 "美国世界新闻与报道（USWNR）顶级医院 "决定）对 DR 住院医师、乳腺成像（BI）、肌肉骨骼（MSK）、介入（IR）和神经放射（NR）研究金项目进行了分析。DR 项目的声誉以同一机构的研究金为替代。如果一个项目达到了配额，则认为该项目已满：2022年的A/P比率分别为1.02（IR）、0.83（BI）、0.75（MSK）和0.88（NR）。BI、MSK和NR奖学金合计填补了78%（529/679）的职位和56%（132/234）的项目。与较高职位填补率相关的因素包括Doximity前25名、Minnie姑妈半决赛选手、USWNR前20名（P均为9）以及研究金配额大于3（P=结论：规模较大、声誉较高的研究金项目和规模较大的基础 DR 住院医师项目更有可能完成其 NRMP 配额。

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引用次数: 0

Addressing Mental Health in Professional Management 在专业管理中解决心理健康问题。

IF 4 3区医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

Journal of the American College of Radiology

Pub Date : 2024-10-01 DOI: 10.1016/j.jacr.2023.08.040

引用次数: 0

A Practical Framework for Promoting Justice-Oriented Innovation in Radiology 促进放射学以正义为导向的创新的实用框架

IF 4 3区医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

Journal of the American College of Radiology

Pub Date : 2024-10-01 DOI: 10.1016/j.jacr.2024.08.007

Jacob A. Blythe MD , Vrushab Gowda MD, JD , Efren J. Flores MD

This article argues that innovation should be guided by the values of the field it hopes to advance. As radiology has come to recognize its role in creating or perpetuating imaging disparities, it has become evident that imaging disparities may arise from innovations that fail to consider impacts on justice from the outset. Acknowledging that a radiologist’s deepest commitments as a physician involve doing no harm, doing good, and ensuring justice, we propose redefining stewardship in radiologic innovation to include a positive obligation to prioritize justice in innovation. Engaging in justice-oriented innovation requires an imaging disparity to be identified, understood, and targeted through a concerted developmental effort. To this end, we consider the structural changes necessary to achieve this objective and provide a template outlining a future for radiologic innovation that aims to mitigate imaging disparities. We provide a simple rubric by which innovative efforts can be deemed “justice-oriented innovations” in environments featuring a surplus of ideas and limited resources to facilitate their prioritization. Innovations that are likely to mitigate imaging disparities should have access to accelerated pipelines for development and commercialization.

本文认为，创新应该以其希望推动的领域的价值观为指导。随着放射学逐渐认识到自己在造成或延续影像差异方面所扮演的角色，我们可以清楚地看到，影像差异可能源于从一开始就没有考虑到对公正的影响的创新。我们认识到，作为一名医生，放射科医生最深切的承诺是不伤害他人、行善并确保公正，因此我们建议重新定义放射创新中的管理职责，将在创新中优先考虑公正这一积极义务纳入其中。参与以正义为导向的创新需要通过协调一致的发展努力来识别、理解和针对成像差异。为此，我们考虑了实现这一目标所需的结构性变化，并提供了一个模板，勾勒出放射学创新的未来，旨在减少影像差异。我们提供了一个简单的标准，在想法过剩、资源有限的环境下，创新努力可根据该标准被视为 "以公正为导向的创新"，以促进其优先次序的确定。有可能缩小影像差距的创新应能加速开发和商业化进程。

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引用次数: 0

Transforming Health Care Landscapes: The Lever of Radiology Research and Innovation on Emerging Markets Poised for Aggressive Growth 改变医疗保健格局：放射学研究与创新对高速增长的新兴市场的影响。

IF 4 3区医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

Journal of the American College of Radiology

Pub Date : 2024-10-01 DOI: 10.1016/j.jacr.2024.07.010

Nicole M. Davis PhD , Ezat El-Said MD , Patrick Fortune PhD , Angela Shen MD, MBA , Marc D. Succi MD

Advances in radiology are crucial not only to the future of the field but to medicine as a whole. Here, we present three emerging areas of medicine that are poised to change how health care is delivered—hospital at home, artificial intelligence, and precision medicine—and illustrate how advances in radiological tools and technologies are helping to fuel the growth of these markets in the United States and across the globe.

放射学的进步不仅对该领域的未来至关重要，而且对整个医学也至关重要。在这里，我们将介绍准备改变医疗服务方式的三个新兴医学领域--家庭医院、人工智能和精准医疗，并说明放射工具和技术的进步如何帮助推动这些市场在美国和全球的发展。

引用次数: 0

Establishing a Validation Infrastructure for Imaging-Based Artificial Intelligence Algorithms Before Clinical Implementation 在临床应用前为基于成像的人工智能算法建立验证基础设施。

IF 4 3区医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

Journal of the American College of Radiology

Pub Date : 2024-10-01 DOI: 10.1016/j.jacr.2024.04.027

With promising artificial intelligence (AI) algorithms receiving FDA clearance, the potential impact of these models on clinical outcomes must be evaluated locally before their integration into routine workflows. Robust validation infrastructures are pivotal to inspecting the accuracy and generalizability of these deep learning algorithms to ensure both patient safety and health equity. Protected health information concerns, intellectual property rights, and diverse requirements of models impede the development of rigorous external validation infrastructures. The authors propose various suggestions for addressing the challenges associated with the development of efficient, customizable, and cost-effective infrastructures for the external validation of AI models at large medical centers and institutions. The authors present comprehensive steps to establish an AI inferencing infrastructure outside clinical systems to examine the local performance of AI algorithms before health practice or systemwide implementation and promote an evidence-based approach for adopting AI models that can enhance radiology workflows and improve patient outcomes.

随着前景广阔的人工智能（AI）算法获得 FDA（美国食品和药物管理局）许可，在将这些模型纳入常规工作流程之前，必须对其对临床结果的潜在影响进行本地评估。强大的验证基础设施对于检测这些深度学习算法的准确性和可推广性以确保患者安全和健康公平至关重要。受保护的健康信息（PHI）问题、知识产权和模型的不同要求阻碍了严格的外部验证基础设施的发展。我们的工作提出了各种建议，以应对与开发高效、可定制和具有成本效益的基础设施相关的挑战，从而在大型医疗中心和机构对人工智能模型进行外部验证。我们提出了在临床系统外建立人工智能推断基础设施的综合步骤，以便在医疗实践或全系统实施之前检查人工智能算法的本地性能，并推广基于证据的方法，以采用可增强放射学工作流程并改善患者预后的人工智能模型。

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引用次数: 0

Assessing Laterality Errors in Radiology: Comparing Generative Artificial Intelligence and Natural Language Processing 评估放射学中的侧影错误：比较生成式人工智能和自然语言处理。

IF 4 3区医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

Journal of the American College of Radiology

Pub Date : 2024-10-01 DOI: 10.1016/j.jacr.2024.06.014

Purpose

We compared the performance of generative artificial intelligence (AI) (Augmented Transformer Assisted Radiology Intelligence [ATARI, Microsoft Nuance, Microsoft Corporation, Redmond, Washington]) and natural language processing (NLP) tools for identifying laterality errors in radiology reports and images.

Methods

We used an NLP-based (mPower, Microsoft Nuance) tool to identify radiology reports flagged for laterality errors in its Quality Assurance Dashboard. The NLP model detects and highlights laterality mismatches in radiology reports. From an initial pool of 1,124 radiology reports flagged by the NLP for laterality errors, we selected and evaluated 898 reports that encompassed radiography, CT, MRI, and ultrasound modalities to ensure comprehensive coverage. A radiologist reviewed each radiology report to assess if the flagged laterality errors were present (reporting error—true-positive) or absent (NLP error—false-positive). Next, we applied ATARI to 237 radiology reports and images with consecutive NLP true-positive (118 reports) and false-positive (119 reports) laterality errors. We estimated accuracy of NLP and generative AI tools to identify overall and modality-wise laterality errors.

Results

Among the 898 NLP-flagged laterality errors, 64% (574 of 898) had NLP errors and 36% (324 of 898) were reporting errors. The text query ATARI feature correctly identified the absence of laterality mismatch (NLP false-positives) with a 97.4% accuracy (115 of 118 reports; 95% confidence interval [CI] = 96.5%-98.3%). Combined vision and text query resulted in 98.3% accuracy (116 of 118 reports or images; 95% CI = 97.6%-99.0%), and query alone had a 98.3% accuracy (116 of 118 images; 95% CI = 97.6%-99.0%).

Conclusion

The generative AI-empowered ATARI prototype outperformed the assessed NLP tool for determining true and false laterality errors in radiology reports while enabling an image-based laterality determination. Underlying errors in ATARI text query in complex radiology reports emphasize the need for further improvement in the technology.

目的：我们比较了生成式人工智能（G-AI，ATARI）和自然语言处理（NLP）工具在识别放射学报告和图像中的侧位错误方面的性能：我们使用基于 NLP（mPower）的工具来识别其 QA 面板中标记为侧位错误的放射学报告。NLP 模型可检测并突出显示放射学报告中的侧位不匹配问题。我们从 NLP 标记为侧位错误的 1124 份放射学报告的初始库中挑选并评估了 898 份报告，其中包括放射摄影、CT、核磁共振成像和超声模式，以确保全面覆盖。一名放射科医生审查了每份放射报告，以评估是否存在标记的侧位错误（报告错误--真阳性）或不存在标记的侧位错误（NLP 错误--假阳性）。接下来，我们将 ATARI 应用于 237 份存在连续 NLP 真阳性（118 份报告）和假阳性（119 份报告）侧位错误的放射学报告和图像。我们估算了NLP和G-AI工具在识别整体和不同模式侧位错误方面的准确性：在898个NLP标记的侧位错误中，64%（574/898）为NLP错误，36%（324/898）为报告错误。文本查询 ATARI 功能能正确识别侧位不匹配（NLP 假阳性），准确率为 97.4%（115/118 份报告；95% CI = 96.5% - 98.3%）。结合视觉和文本查询的准确率为 98.3%（116/118 份报告/图片；95% CI = 97.6% - 99.0%），单独查询的准确率为 98.3%（116/118 张图片；95% CI = 97.6% - 99.0%）：结论：在确定放射学报告中的真假侧位错误方面，生成式人工智能驱动的 ATARI 原型优于经过评估的 NLP 工具，同时还能进行基于图像的侧位确定。在复杂的放射学报告中，ATARI文本查询的潜在错误强调了进一步改进该技术的必要性。

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引用次数: 0

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Journal of the American College of Radiology

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