Radiologie (Heidelberg, Germany)最新文献_第6页

[Artificial intelligence in radiology : Literature overview and reading recommendations]. [放射学中的人工智能：文献综述和阅读建议]。

Radiologie (Heidelberg, Germany) Pub Date : 2025-04-01 Epub Date: 2025-02-04 DOI: 10.1007/s00117-025-01419-z

Moritz C Halfmann, Peter Mildenberger, Tobias Jorg

{"title":"[Artificial intelligence in radiology : Literature overview and reading recommendations].","authors":"Moritz C Halfmann, Peter Mildenberger, Tobias Jorg","doi":"10.1007/s00117-025-01419-z","DOIUrl":"10.1007/s00117-025-01419-z","url":null,"abstract":"Background: Due to the ongoing rapid advancement of artificial intelligence (AI), including large language models (LLMs), radiologists will soon face the challenge of the responsible clinical integration of these models.Objectives: The aim of this work is to provide an overview of current developments regarding LLMs, potential applications in radiology, and their (future) relevance and limitations.Materials and methods: This review analyzes publications on LLMs for specific applications in medicine and radiology. Additionally, literature related to the challenges of clinical LLM use was reviewed and summarized.Results: In addition to a general overview of current literature on radiological applications of LLMs, several particularly noteworthy studies on the subject are recommended.Conclusions: In order to facilitate the forthcoming clinical integration of LLMs, radiologists need to engage with the topic, understand various application areas, and be aware of potential limitations in order to address challenges related to patient safety, ethics, and data protection.","PeriodicalId":74635,"journal":{"name":"Radiologie (Heidelberg, Germany)","volume":" ","pages":"266-270"},"PeriodicalIF":0.0,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143191497","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

[The peppered brain stem : A neuroradiological puzzle]. [胡椒状脑干：一个神经放射学难题]。

Radiologie (Heidelberg, Germany) Pub Date : 2025-04-01 Epub Date: 2025-02-13 DOI: 10.1007/s00117-025-01423-3

Lisa Ullrich, Torleif Sandner, Thomas Helmberger

引用次数: 0

[Misdiagnoses in the diagnostics of the abdomen and pelvis]. 【腹部和骨盆的误诊】。

Radiologie (Heidelberg, Germany) Pub Date : 2025-04-01 Epub Date: 2025-02-26 DOI: 10.1007/s00117-025-01425-1

Andreas G Schreyer, Markus Juchems

引用次数: 0

[Technical foundations of large language models]. [大型语言模型的技术基础]。

Radiologie (Heidelberg, Germany) Pub Date : 2025-04-01 Epub Date: 2025-03-10 DOI: 10.1007/s00117-025-01427-z

Christian Blüthgen

{"title":"[Technical foundations of large language models].","authors":"Christian Blüthgen","doi":"10.1007/s00117-025-01427-z","DOIUrl":"10.1007/s00117-025-01427-z","url":null,"abstract":"Background: Large language models (LLMs) such as ChatGPT have rapidly revolutionized the way computers can analyze human language and the way we can interact with computers.Objective: To give an overview of the emergence and basic principles of computational language models.Methods: Narrative literature-based analysis of the history of the emergence of language models, the technical foundations, the training process and the limitations of LLMs.Results: Nowadays, LLMs are mostly based on transformer models that can capture context through their attention mechanism. Through a multistage training process with comprehensive pretraining, supervised fine-tuning and alignment with human preferences, LLMs have developed a general understanding of language. This enables them to flexibly analyze texts and produce outputs of high linguistic quality.Conclusion: Their technical foundations and training process make large language models versatile general-purpose tools for text processing, with numerous applications in radiology. The main limitation is the tendency to postulate incorrect but plausible-sounding information with high confidence.","PeriodicalId":74635,"journal":{"name":"Radiologie (Heidelberg, Germany)","volume":" ","pages":"227-234"},"PeriodicalIF":0.0,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11937190/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143588441","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

[Integration of large language models into the clinic : Revolution in analysing and processing patient data to increase efficiency and quality in radiology]. [将大型语言模型集成到临床：分析和处理患者数据以提高放射学效率和质量的革命]。

Radiologie (Heidelberg, Germany) Pub Date : 2025-04-01 Epub Date: 2025-03-12 DOI: 10.1007/s00117-025-01431-3

Philipp Arnold, Maurice Henkel, Fabian Bamberg, Elmar Kotter

{"title":"[Integration of large language models into the clinic : Revolution in analysing and processing patient data to increase efficiency and quality in radiology].","authors":"Philipp Arnold, Maurice Henkel, Fabian Bamberg, Elmar Kotter","doi":"10.1007/s00117-025-01431-3","DOIUrl":"10.1007/s00117-025-01431-3","url":null,"abstract":"Background: Large Language Models (LLMs) like ChatGPT, Llama and Claude are transforming healthcare by interpreting complex text, extracting information, and providing guideline-based support. Radiology, with its high patient volume and digital workflows, is a ideal field for LLM integration.Objective: Assessment of the potential of LLMs to enhance efficiency, standardization, and decision support in radiology, while addressing ethical and regulatory challenges.Material and methods: Pilot studies at Freiburg and Basel university hospitals evaluated local LLM systems for tasks like prior report summarization and guideline-driven reporting. Integration with Picture Archiving and Communication System (PACS) and Electronic Health Record (EHR) systems was achieved via Digital Imaging and Communications in Medicine (DICOM) and Fast Healthcare Interoperability Resources (FHIR) standards. Metrics included time savings, compliance with the European Union (EU) Artificial Intelligence (AI) Act, and user acceptance.Results: LLMs demonstrate significant potential as a support tool for radiologists in clinical practice by reducing reporting times, automating routine tasks, and ensuring consistent, high-quality results. They also support interdisciplinary workflows (e.g., tumor boards) and meet data protection requirements when locally implemented.Discussion: Local LLM systems are feasible and beneficial in radiology, enhancing efficiency and diagnostic quality. Future work should refine transparency, expand applications, and ensure LLMs complement medical expertise while adhering to ethical and legal standards.","PeriodicalId":74635,"journal":{"name":"Radiologie (Heidelberg, Germany)","volume":" ","pages":"243-248"},"PeriodicalIF":0.0,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143617953","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

[Large Language Models/Generative AI]. [大型语言模型/生成式AI]。

Radiologie (Heidelberg, Germany) Pub Date : 2025-04-01 Epub Date: 2025-03-25 DOI: 10.1007/s00117-025-01434-0

Elmar Kotter, Christian Herold

引用次数: 0

[Transformation of free-text radiology reports into structured data]. [将自由文本放射学报告转换为结构化数据]。

Radiologie (Heidelberg, Germany) Pub Date : 2025-04-01 Epub Date: 2025-02-11 DOI: 10.1007/s00117-025-01422-4

Markus M Graf, Keno K Bressem, Lisa C Adams

{"title":"[Transformation of free-text radiology reports into structured data].","authors":"Markus M Graf, Keno K Bressem, Lisa C Adams","doi":"10.1007/s00117-025-01422-4","DOIUrl":"10.1007/s00117-025-01422-4","url":null,"abstract":"Background: The rapid development of large language models (LLMs) opens up new possibilities for the automated processing of medical texts. Transforming unstructured radiology reports into structured data is crucial for efficient use in clinical decision support systems, research, and improving patient care.Objectives: What are the challenges of transforming natural language radiology reports into structured data using LLMs? Which methods and architectures are promising? How can the quality and reliability of the extracted data be ensured?Materials and methods: This article examines current research on the application of LLMs in radiological information processing. Various approaches such as rule-based systems, machine learning, and deep learning models, particularly neural network architectures, are analyzed and compared. The focus is on extracting information such as diagnoses, anatomical locations, findings, and measurements.Results and conclusion: LLMs show great potential in transforming reports into structured data. In particular, deep learning models trained on large datasets achieve high accuracies. However, challenges remain, such as dealing with ambiguities, abbreviations, and the variability of linguistic expressions. Combining LLMs with domain-specific knowledge, for example, in the form of ontologies, can further improve the performance of the systems. Integrating contextual information and developing robust evaluation metrics are also important research directions.","PeriodicalId":74635,"journal":{"name":"Radiologie (Heidelberg, Germany)","volume":" ","pages":"249-256"},"PeriodicalIF":0.0,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143400985","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

[Optimized interaction with Large Language Models : A practical guide to Prompt Engineering and Retrieval-Augmented Generation]. [与大型语言模型的优化交互：提示工程和检索增强生成的实用指南]。

Radiologie (Heidelberg, Germany) Pub Date : 2025-04-01 Epub Date: 2025-02-21 DOI: 10.1007/s00117-025-01416-2

Anna Fink, Alexander Rau, Elmar Kotter, Fabian Bamberg, Maximilian Frederik Russe

{"title":"[Optimized interaction with Large Language Models : A practical guide to Prompt Engineering and Retrieval-Augmented Generation].","authors":"Anna Fink, Alexander Rau, Elmar Kotter, Fabian Bamberg, Maximilian Frederik Russe","doi":"10.1007/s00117-025-01416-2","DOIUrl":"10.1007/s00117-025-01416-2","url":null,"abstract":"Background: Given the increasing number of radiological examinations, large language models (LLMs) offer promising support in radiology. Optimized interaction is essential to ensure reliable results.Objectives: This article provides an overview of interaction techniques such as prompt engineering, zero-shot learning, and retrieval-augmented generation (RAG) and gives practical tips for their application in radiology.Materials and methods: Demonstration of interaction techniques based on practical examples with concrete recommendations for their application in routine radiological practice.Results: Advanced interaction techniques allow task-specific adaptation of LLMs without the need for retraining. The creation of precise prompts and the use of zero-shot and few-shot learning can significantly improve response quality. RAG enables the integration of current and domain-specific information into LLM tools, increasing the accuracy and relevance of the generated content.Conclusions: The use of prompt engineering, zero-shot and few-shot learning, and RAG can optimize interaction with LLMs in radiology. Through these targeted strategies, radiologists can efficiently integrate general chatbots into routine practice to improve patient care.","PeriodicalId":74635,"journal":{"name":"Radiologie (Heidelberg, Germany)","volume":" ","pages":"235-242"},"PeriodicalIF":0.0,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143470156","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

[Temporal bone surgery and requirements for radiology]. [颞骨手术及放射学要求]。

Radiologie (Heidelberg, Germany) Pub Date : 2025-03-28 DOI: 10.1007/s00117-025-01429-x

Alessandro Bozzato

引用次数: 0

[Parametric imaging of the breast-a paradigm shift?] 乳房参数化成像——范式转变？］

Radiologie (Heidelberg, Germany) Pub Date : 2025-03-01 Epub Date: 2025-02-21 DOI: 10.1007/s00117-025-01420-6

Maximilian Reiser, R Schulz-Wendtland

引用次数: 0