{"title":"放射学工作流程中的大型语言模型:德国医疗保健系统中非视觉任务的生成人工智能的探索性研究。","authors":"Stefanie Steinhauser , Sabrina Welsch","doi":"10.1016/j.healthpol.2025.105444","DOIUrl":null,"url":null,"abstract":"<div><h3>Background</h3><div>Large language models (LLMs) are gaining attention for their potential to enhance radiology workflows by addressing challenges such as increasing workloads and staff shortages. However, limited knowledge among radiologists and concerns about their practical implementation and ethical implications present challenges.</div></div><div><h3>Objective</h3><div>This study investigates radiologists' perspectives on the use of LLMs, exploring their potential benefits, challenges, and impact on workflows and professional roles.</div></div><div><h3>Methods</h3><div>An exploratory, qualitative study was conducted using 12 semi-structured interviews with radiology experts. Data were analyzed to assess participants' awareness, attitudes, and perceived applications of LLMs in radiology.</div></div><div><h3>Results</h3><div>LLMs were identified as promising tools for reducing workloads by streamlining tasks like summarizing clinical histories and generating standardized reports, improving communication and efficiency. Participants expressed openness to LLM integration but noted concerns about their impact on human interaction, ethical standards, and liability. The role of radiologists is expected to evolve with LLM adoption, with a shift toward data stewardship and interprofessional collaboration. Barriers to implementation included limited awareness, regulatory constraints, and outdated infrastructure.</div></div><div><h3>Conclusions</h3><div>The integration of LLMs is hindered by regulatory challenges, outdated infrastructure, and limited awareness among radiologists. Policymakers should establish clear, practical regulations to address liability and ethical concerns while ensuring compliance with privacy standards. Investments in modernizing clinical infrastructure and expanding training programs are critical to enable radiologists to effectively use these tools. By addressing these barriers, LLMs can enhance efficiency, reduce workloads, and improve patient care, while preserving the central role of radiologists in diagnostic and therapeutic processes.</div></div>","PeriodicalId":55067,"journal":{"name":"Health Policy","volume":"161 ","pages":"Article 105444"},"PeriodicalIF":3.4000,"publicationDate":"2025-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Large language models in radiology workflows: An exploratory study of generative AI for non-visual tasks in the German healthcare system\",\"authors\":\"Stefanie Steinhauser , Sabrina Welsch\",\"doi\":\"10.1016/j.healthpol.2025.105444\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><h3>Background</h3><div>Large language models (LLMs) are gaining attention for their potential to enhance radiology workflows by addressing challenges such as increasing workloads and staff shortages. However, limited knowledge among radiologists and concerns about their practical implementation and ethical implications present challenges.</div></div><div><h3>Objective</h3><div>This study investigates radiologists' perspectives on the use of LLMs, exploring their potential benefits, challenges, and impact on workflows and professional roles.</div></div><div><h3>Methods</h3><div>An exploratory, qualitative study was conducted using 12 semi-structured interviews with radiology experts. Data were analyzed to assess participants' awareness, attitudes, and perceived applications of LLMs in radiology.</div></div><div><h3>Results</h3><div>LLMs were identified as promising tools for reducing workloads by streamlining tasks like summarizing clinical histories and generating standardized reports, improving communication and efficiency. Participants expressed openness to LLM integration but noted concerns about their impact on human interaction, ethical standards, and liability. The role of radiologists is expected to evolve with LLM adoption, with a shift toward data stewardship and interprofessional collaboration. Barriers to implementation included limited awareness, regulatory constraints, and outdated infrastructure.</div></div><div><h3>Conclusions</h3><div>The integration of LLMs is hindered by regulatory challenges, outdated infrastructure, and limited awareness among radiologists. Policymakers should establish clear, practical regulations to address liability and ethical concerns while ensuring compliance with privacy standards. Investments in modernizing clinical infrastructure and expanding training programs are critical to enable radiologists to effectively use these tools. By addressing these barriers, LLMs can enhance efficiency, reduce workloads, and improve patient care, while preserving the central role of radiologists in diagnostic and therapeutic processes.</div></div>\",\"PeriodicalId\":55067,\"journal\":{\"name\":\"Health Policy\",\"volume\":\"161 \",\"pages\":\"Article 105444\"},\"PeriodicalIF\":3.4000,\"publicationDate\":\"2025-09-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Health Policy\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S016885102500199X\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"HEALTH CARE SCIENCES & SERVICES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Health Policy","FirstCategoryId":"3","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S016885102500199X","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"HEALTH CARE SCIENCES & SERVICES","Score":null,"Total":0}
Large language models in radiology workflows: An exploratory study of generative AI for non-visual tasks in the German healthcare system
Background
Large language models (LLMs) are gaining attention for their potential to enhance radiology workflows by addressing challenges such as increasing workloads and staff shortages. However, limited knowledge among radiologists and concerns about their practical implementation and ethical implications present challenges.
Objective
This study investigates radiologists' perspectives on the use of LLMs, exploring their potential benefits, challenges, and impact on workflows and professional roles.
Methods
An exploratory, qualitative study was conducted using 12 semi-structured interviews with radiology experts. Data were analyzed to assess participants' awareness, attitudes, and perceived applications of LLMs in radiology.
Results
LLMs were identified as promising tools for reducing workloads by streamlining tasks like summarizing clinical histories and generating standardized reports, improving communication and efficiency. Participants expressed openness to LLM integration but noted concerns about their impact on human interaction, ethical standards, and liability. The role of radiologists is expected to evolve with LLM adoption, with a shift toward data stewardship and interprofessional collaboration. Barriers to implementation included limited awareness, regulatory constraints, and outdated infrastructure.
Conclusions
The integration of LLMs is hindered by regulatory challenges, outdated infrastructure, and limited awareness among radiologists. Policymakers should establish clear, practical regulations to address liability and ethical concerns while ensuring compliance with privacy standards. Investments in modernizing clinical infrastructure and expanding training programs are critical to enable radiologists to effectively use these tools. By addressing these barriers, LLMs can enhance efficiency, reduce workloads, and improve patient care, while preserving the central role of radiologists in diagnostic and therapeutic processes.
期刊介绍:
Health Policy is intended to be a vehicle for the exploration and discussion of health policy and health system issues and is aimed in particular at enhancing communication between health policy and system researchers, legislators, decision-makers and professionals concerned with developing, implementing, and analysing health policy, health systems and health care reforms, primarily in high-income countries outside the U.S.A.