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Large language models (LLMs) in radiology exams for medical students: Performance and consequences. 医学生放射学考试中的大型语言模型(LLM):成绩与后果
IF 1.3 4区 医学 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING Pub Date : 2024-11-04 DOI: 10.1055/a-2437-2067
Jennifer Gotta, Quang Anh Le Hong, Vitali Koch, Leon D Gruenewald, Tobias Geyer, Simon S Martin, Jan-Erik Scholtz, Christian Booz, Daniel Pinto Dos Santos, Scherwin Mahmoudi, Katrin Eichler, Tatjana Gruber-Rouh, Renate Hammerstingl, Teodora Biciusca, Lisa Joy Juergens, Elena Höhne, Christoph Mader, Thomas J Vogl, Philipp Reschke

The evolving field of medical education is being shaped by technological advancements, including the integration of Large Language Models (LLMs) like ChatGPT. These models could be invaluable resources for medical students, by simplifying complex concepts and enhancing interactive learning by providing personalized support. LLMs have shown impressive performance in professional examinations, even without specific domain training, making them particularly relevant in the medical field. This study aims to assess the performance of LLMs in radiology examinations for medical students, thereby shedding light on their current capabilities and implications.This study was conducted using 151 multiple-choice questions, which were used for radiology exams for medical students. The questions were categorized by type and topic and were then processed using OpenAI's GPT-3.5 and GPT- 4 via their API, or manually put into Perplexity AI with GPT-3.5 and Bing. LLM performance was evaluated overall, by question type and by topic.GPT-3.5 achieved a 67.6% overall accuracy on all 151 questions, while GPT-4 outperformed it significantly with an 88.1% overall accuracy (p<0.001). GPT-4 demonstrated superior performance in both lower-order and higher-order questions compared to GPT-3.5, Perplexity AI, and medical students, with GPT-4 particularly excelling in higher-order questions. All GPT models would have successfully passed the radiology exam for medical students at our university.In conclusion, our study highlights the potential of LLMs as accessible knowledge resources for medical students. GPT-4 performed well on lower-order as well as higher-order questions, making ChatGPT-4 a potentially very useful tool for reviewing radiology exam questions. Radiologists should be aware of ChatGPT's limitations, including its tendency to confidently provide incorrect responses. · ChatGPT demonstrated remarkable performance, achieving a passing grade on a radiology examination for medical students that did not include image questions.. · GPT-4 exhibits significantly improved performance compared to its predecessors GPT-3.5 and Perplexity AI with 88% of questions answered correctly.. · Radiologists as well as medical students should be aware of ChatGPT's limitations, including its tendency to confidently provide incorrect responses.. · Gotta J, Le Hong QA, Koch V et al. Large language models (LLMs) in radiology exams for medical students: Performance and consequences. Fortschr Röntgenstr 2024; DOI 10.1055/a-2437-2067.

不断发展的医学教育领域正受到技术进步的影响,其中包括像 ChatGPT 这样的大语言模型(LLM)的整合。这些模型可以简化复杂的概念,并通过提供个性化支持加强互动学习,是医学生的宝贵资源。即使没有接受过特定领域的培训,LLMs 在专业考试中的表现也令人印象深刻,因此它们在医学领域尤为重要。本研究旨在评估法学硕士在医学生放射学考试中的表现,从而揭示他们目前的能力和意义。本研究使用了 151 道用于医学生放射学考试的多项选择题。这些问题按类型和主题分类,然后使用 OpenAI 的 GPT-3.5 和 GPT- 4 通过其 API 进行处理,或通过 GPT-3.5 和 Bing 手动输入 Perplexity AI。我们按问题类型和主题对 LLM 的整体性能进行了评估。GPT-3.5 在所有 151 个问题上的整体准确率为 67.6%,而 GPT-4 的表现明显优于 GPT-3.5,整体准确率为 88.1%(P
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引用次数: 0
Position Paper of the German Society for Interventional Radiology and Minimally Invasive Therapy (DeGIR) and the German Roentgen Society (DRG) on Structural and Professional Requirements in Interventional Oncology. 德国介入放射学和微创治疗学会 (DeGIR) 和德国伦琴射线学会 (DRG) 关于介入肿瘤学的结构和专业要求的立场文件。
IF 1.3 4区 医学 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING Pub Date : 2024-11-04 DOI: 10.1055/a-2373-1013
Peter Isfort, Christof M Sommer, Philipp Bruners, Bettina Maiwald, Jens-Peter Kühn, Christoph Georg Radosa, Roman Kloeckner, Patrick Freyhardt, Mareike Franke, Michael Moche, Ralf-Thorsten Hoffmann, Konstantin Nikolaou, Andreas H Mahnken, Marcus Katoh
<p><p>Interventional oncology (IO) employs various techniques to enable minimally invasive, image-guided treatment of tumor diseases with both curative and palliative goals. Additionally, it significantly contributes to managing tumor-related and perioperative complications, offering diverse supportive procedures for patients at all stages of their diseases. The execution of IO procedures places unique demands on the equipment, personnel, and structural organization of radiological clinics, necessitating specific expertise from interventional radiologists.This position paper aims to comprehensively outline the multifaceted aspects of IO and discuss the requisite criteria for hospitals, radiological clinics, and interventional radiologists (IRs). Furthermore, it underscores overarching considerations of quality assurance that clinics and professional societies should prioritize.The requirements for hospitals, radiological clinics, and IRs are varied and demand not only a high level of proficiency in performing IO procedures but also in-depth knowledge of the differential therapy for various tumor diseases. This expertise is essential for effectively serving as clinical partners in the interdisciplinary treatment of oncologic patients. Additionally, a thorough understanding and safe handling of ionizing radiation technologies, along with proficiency in radiation protection methods, which are fundamental aspects of radiological specialist training, is crucial for ensuring the safety of IO procedures for both patients and staff. The Deutsche Gesellschaft für Interventionelle Radiologie und minimal-invasive Therapie (DeGIR) and the Cardiovascular and Interventional Radiological Society of Europe (CIRSE) have long-established dedicated quality management programs, accrediting radiology clinics and certifying IRs. These initiatives aim to uphold the highest standards of care and meet the quality expectations set by politics in healthcare system, particularly in the realm of interventional radiology. · The various procedures in the field of interventional oncology (IO) are complex medical interventions that require not only the most advanced technical equipment but also adequate human resources, particularly specialized expertise in interventional radiology, diagnostic imaging, oncology, and radiation protection.. · This expertise is an integral part of the specialized medical training in radiology and is certified by professional societies such as the German Society for Interventional Radiology (DeGIR) and the Cardiovascular and Interventional Radiological Society of Europe (CIRSE).. · Professional societies like DeGIR, CIRSE, and the American Society of Interventional Radiology (SIR) establish the necessary quality assurance framework for comprehensive, high-quality IO therapy through quality assurance (QA) registries, standard operating procedure (SOP) documents, and participation in guideline development.. · Currently, radiology is the only disciplin
介入肿瘤学(IO)采用各种技术,在图像引导下对肿瘤疾病进行微创治疗,以达到治愈和姑息的目的。此外,介入肿瘤学还在控制肿瘤相关并发症和围手术期并发症方面做出了重大贡献,为处于疾病各个阶段的患者提供各种支持性治疗。本立场文件旨在全面概述 IO 的方方面面,并讨论医院、放射诊所和介入放射医师(IRs)的必要标准。对医院、放射科诊所和介入放射医师的要求多种多样,不仅要求他们熟练掌握 IO 手术,还要求他们对各种肿瘤疾病的鉴别治疗有深入的了解。这些专业知识对于在肿瘤患者的跨学科治疗中有效发挥临床合作伙伴的作用至关重要。此外,对电离辐射技术的透彻了解和安全操作,以及熟练掌握辐射防护方法(这是放射科专家培训的基本内容),对于确保 IO 程序对患者和工作人员的安全至关重要。德国介入放射学与微创治疗协会(DeGIR)和欧洲心血管与介入放射学会(CIRSE)长期以来一直致力于质量管理计划,对放射诊所进行认证,对介入放射医师进行认证。这些举措旨在坚持最高的医疗标准,满足医疗系统,尤其是介入放射学领域的政治对质量的期望。- 介入肿瘤学(IO)领域的各种程序都是复杂的医疗干预措施,不仅需要最先进的技术设备,还需要充足的人力资源,特别是介入放射学、诊断成像、肿瘤学和辐射防护方面的专业知识。- 这些专业知识是放射学专业医学培训不可分割的一部分,并由德国介入放射学会(DeGIR)和欧洲心血管和介入放射学会(CIRSE)等专业学会认证。- 德国介入放射学会(DeGIR)、欧洲心血管与介入放射学会(CIRSE)和美国介入放射学会(SIR)等专业学会通过质量保证(QA)登记册、标准操作程序(SOP)文件和参与指南制定,为全面、高质量的 IO 治疗建立了必要的质量保证框架。- 目前,放射学是唯一一门通过专门的培训计划和量身定制的认证程序为医生提供理论和实践知识、技能和能力的学科,这些知识、技能和能力是执行 IO 领域高要求程序所必需的。- Isfort P、Sommer CM、Bruners P 等人. 德国介入放射学和微创治疗学会 (DeGIR) 和德国伦琴学会 (DRG) 关于介入肿瘤学的结构和专业要求的立场文件。Fortschr Röntgenstr 2024; DOI 10.1055/a-2373-1013.
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引用次数: 0
[Thoracic penetrating aortic ulcer: conventional radiography as a valuable diagnostic tool]. [胸腔穿透性主动脉溃疡:作为重要诊断工具的常规放射摄影术]。
IF 1.3 4区 医学 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING Pub Date : 2024-11-04 DOI: 10.1055/a-2441-5303
Fiona Mankertz, Eiko Rathmann, Alexandra Busemann
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引用次数: 0
Improving the use of LLMs in radiology through prompt engineering: from precision prompts to zero-shot learning. 通过提示工程改善 LLM 在放射学中的应用:从精确提示到零镜头学习。
IF 1.3 4区 医学 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING Pub Date : 2024-11-01 Epub Date: 2024-02-26 DOI: 10.1055/a-2264-5631
Maximilian Frederik Russe, Marco Reisert, Fabian Bamberg, Alexander Rau

Purpose:  Large language models (LLMs) such as ChatGPT have shown significant potential in radiology. Their effectiveness often depends on prompt engineering, which optimizes the interaction with the chatbot for accurate results. Here, we highlight the critical role of prompt engineering in tailoring the LLMs' responses to specific medical tasks.

Materials and methods:  Using a clinical case, we elucidate different prompting strategies to adapt the LLM ChatGPT using GPT4 to new tasks without additional training of the base model. These approaches range from precision prompts to advanced in-context methods such as few-shot and zero-shot learning. Additionally, the significance of embeddings, which serve as a data representation technique, is discussed.

Results:  Prompt engineering substantially improved and focused the chatbot's output. Moreover, embedding of specialized knowledge allows for more transparent insight into the model's decision-making and thus enhances trust.

Conclusion:  Despite certain challenges, prompt engineering plays a pivotal role in harnessing the potential of LLMs for specialized tasks in the medical domain, particularly radiology. As LLMs continue to evolve, techniques like few-shot learning, zero-shot learning, and embedding-based retrieval mechanisms will become indispensable in delivering tailored outputs.

Key points:   · Large language models might impact radiological practice and decision-masking.. · However, implementation and performance are dependent on the assigned task.. · Optimization of prompting strategies can substantially improve model performance.. · Strategies for prompt engineering range from precision prompts to zero-shot learning..

Citation format: · Russe MF, Reisert M, Bamberg F et al. Improving the use of LLMs in radiology through prompt engineering: from precision prompts to zero-shot learning . Fortschr Röntgenstr 2024; 196: 1166 - 1170.

目的:大型语言模型(LLM),如 ChatGPT,在放射学领域已显示出巨大潜力。它们的有效性通常取决于提示工程,即优化与聊天机器人的交互以获得准确结果。在此,我们强调了提示工程在调整 LLMs 对特定医疗任务的响应方面的关键作用:通过一个临床案例,我们阐明了不同的提示策略,以便在不对基础模型进行额外训练的情况下,让使用 GPT4 的 LLM ChatGPT 适应新任务。这些方法包括从精确提示到高级上下文方法(如少发学习和零发学习)。此外,还讨论了作为数据表示技术的嵌入的意义:结果:提示工程大大改进了聊天机器人的输出,并使其更加集中。此外,专业知识的嵌入可以更透明地洞察模型的决策,从而提高信任度:尽管存在一些挑战,但提示工程在利用 LLMs 的潜力完成医疗领域(尤其是放射学领域)的专业任务方面发挥了关键作用。随着 LLMs 的不断发展,少数几次学习、零次学习和基于嵌入的检索机制等技术将成为提供定制输出不可或缺的技术:- 大型语言模型可能会对放射学实践和决策屏蔽产生影响。- 然而,实施和性能取决于分配的任务。- 优化提示策略可大幅提高模型性能。- 提示工程的策略从精确提示到零点学习不等。
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引用次数: 0
Strahlenschutzkurse der Röntgen Akademie. 伦琴学院的辐射防护课程。
IF 1.3 4区 医学 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING Pub Date : 2024-11-01 Epub Date: 2024-10-25 DOI: 10.1055/a-2406-1148
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引用次数: 0
Die NIS-2-Richtlinie der EU und ihre Umsetzung in nationales Recht – Neue Vorgaben zur Cybersicherheit in der Arztpraxis ab 2025. 欧盟 NIS 2 指令及其在国内法中的应用 - 从 2025 年起对医疗机构网络安全的新要求。
IF 1.3 4区 医学 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING Pub Date : 2024-11-01 Epub Date: 2024-10-25 DOI: 10.1055/a-2406-1023
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引用次数: 0
Two rare vascular findings in one patient: Retro-psoas Iliac Artery & Common Pulmonary Venous Confluence. 一名患者的两种罕见血管发现:髂后动脉和肺总静脉汇合。
IF 1.3 4区 医学 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING Pub Date : 2024-11-01 Epub Date: 2024-05-13 DOI: 10.1055/a-2311-0043
Bastian Schulz, André Euler
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引用次数: 0
Last Call: Noch bis zum 4. November 2024 Abstracts zum RÖKO 2025 einreichen! 最后通知:提交 2025 年 RÖKO 会议论文摘要的截止日期为 2024 年 11 月 4 日!
IF 1.3 4区 医学 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING Pub Date : 2024-11-01 Epub Date: 2024-10-25 DOI: 10.1055/a-2406-0900
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引用次数: 0
Weg von der Bordsteinkante. 远离路缘石
IF 1.3 4区 医学 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING Pub Date : 2024-11-01 Epub Date: 2024-10-25 DOI: 10.1055/a-2406-1189
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引用次数: 0
[Stent-PTA of tumor-related venous obstructions]. [肿瘤相关静脉阻塞的支架-PTA]。
IF 1.3 4区 医学 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING Pub Date : 2024-11-01 Epub Date: 2024-04-04 DOI: 10.1055/a-2284-5587
Johannes Beat Fingerhut, Charlotte Kulka, Michael Doppler, Katharina Vogt, Wibke Uller, Niklas Verloh
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引用次数: 0
期刊
Rofo-fortschritte Auf Dem Gebiet Der Rontgenstrahlen Und Der Bildgebenden Verfahren
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