人工智能提高患者对放射学报告的理解。

IF 2.5 3区 工程技术 Q2 BIOLOGY Yale Journal of Biology and Medicine Pub Date : 2023-09-29 eCollection Date: 2023-09-01 DOI:10.59249/NKOY5498
Kanhai Amin, Pavan Khosla, Rushabh Doshi, Sophie Chheang, Howard P Forman
{"title":"人工智能提高患者对放射学报告的理解。","authors":"Kanhai Amin, Pavan Khosla, Rushabh Doshi, Sophie Chheang, Howard P Forman","doi":"10.59249/NKOY5498","DOIUrl":null,"url":null,"abstract":"<p><p>Diagnostic imaging reports are generally written with a target audience of other providers. As a result, the reports are written with medical jargon and technical detail to ensure accurate communication. With implementation of the 21st Century Cures Act, patients have greater and quicker access to their imaging reports, but these reports are still written above the comprehension level of the average patient. Consequently, many patients have requested reports to be conveyed in language accessible to them. Numerous studies have shown that improving patient understanding of their condition results in better outcomes, so driving comprehension of imaging reports is essential. Summary statements, second reports, and the inclusion of the radiologist's phone number have been proposed, but these solutions have implications for radiologist workflow. Artificial intelligence (AI) has the potential to simplify imaging reports without significant disruptions. Many AI technologies have been applied to radiology reports in the past for various clinical and research purposes, but patient focused solutions have largely been ignored. New natural language processing technologies and large language models (LLMs) have the potential to improve patient understanding of their imaging reports. However, LLMs are a nascent technology and significant research is required before LLM-driven report simplification is used in patient care.</p>","PeriodicalId":48617,"journal":{"name":"Yale Journal of Biology and Medicine","volume":"96 3","pages":"407-417"},"PeriodicalIF":2.5000,"publicationDate":"2023-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/02/b7/yjbm_96_3_407.PMC10524809.pdf","citationCount":"0","resultStr":"{\"title\":\"Artificial Intelligence to Improve Patient Understanding of Radiology Reports.\",\"authors\":\"Kanhai Amin, Pavan Khosla, Rushabh Doshi, Sophie Chheang, Howard P Forman\",\"doi\":\"10.59249/NKOY5498\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Diagnostic imaging reports are generally written with a target audience of other providers. As a result, the reports are written with medical jargon and technical detail to ensure accurate communication. With implementation of the 21st Century Cures Act, patients have greater and quicker access to their imaging reports, but these reports are still written above the comprehension level of the average patient. Consequently, many patients have requested reports to be conveyed in language accessible to them. Numerous studies have shown that improving patient understanding of their condition results in better outcomes, so driving comprehension of imaging reports is essential. Summary statements, second reports, and the inclusion of the radiologist's phone number have been proposed, but these solutions have implications for radiologist workflow. Artificial intelligence (AI) has the potential to simplify imaging reports without significant disruptions. Many AI technologies have been applied to radiology reports in the past for various clinical and research purposes, but patient focused solutions have largely been ignored. New natural language processing technologies and large language models (LLMs) have the potential to improve patient understanding of their imaging reports. However, LLMs are a nascent technology and significant research is required before LLM-driven report simplification is used in patient care.</p>\",\"PeriodicalId\":48617,\"journal\":{\"name\":\"Yale Journal of Biology and Medicine\",\"volume\":\"96 3\",\"pages\":\"407-417\"},\"PeriodicalIF\":2.5000,\"publicationDate\":\"2023-09-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/02/b7/yjbm_96_3_407.PMC10524809.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Yale Journal of Biology and Medicine\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.59249/NKOY5498\",\"RegionNum\":3,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2023/9/1 0:00:00\",\"PubModel\":\"eCollection\",\"JCR\":\"Q2\",\"JCRName\":\"BIOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Yale Journal of Biology and Medicine","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.59249/NKOY5498","RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2023/9/1 0:00:00","PubModel":"eCollection","JCR":"Q2","JCRName":"BIOLOGY","Score":null,"Total":0}
引用次数: 0

摘要

诊断成像报告通常是与其他提供者的目标受众一起编写的。因此,报告采用了医学术语和技术细节,以确保准确的沟通。随着《21世纪治疗法》的实施,患者可以更多、更快地获得他们的影像学报告,但这些报告的撰写仍然高于普通患者的理解水平。因此,许多患者要求用他们能够理解的语言传达报告。大量研究表明,提高患者对病情的理解会带来更好的结果,因此推动对成像报告的理解至关重要。已经提出了总结陈述、第二次报告和包括放射科医生的电话号码,但这些解决方案对放射科医生工作流程有影响。人工智能(AI)有可能在不造成重大干扰的情况下简化成像报告。过去,许多人工智能技术已被应用于放射学报告,用于各种临床和研究目的,但以患者为中心的解决方案在很大程度上被忽视了。新的自然语言处理技术和大型语言模型(LLM)有可能提高患者对其成像报告的理解。然而,LLM是一项新兴技术,在将LLM驱动的报告简化用于患者护理之前,还需要进行大量研究。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

摘要图片

摘要图片

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Artificial Intelligence to Improve Patient Understanding of Radiology Reports.

Diagnostic imaging reports are generally written with a target audience of other providers. As a result, the reports are written with medical jargon and technical detail to ensure accurate communication. With implementation of the 21st Century Cures Act, patients have greater and quicker access to their imaging reports, but these reports are still written above the comprehension level of the average patient. Consequently, many patients have requested reports to be conveyed in language accessible to them. Numerous studies have shown that improving patient understanding of their condition results in better outcomes, so driving comprehension of imaging reports is essential. Summary statements, second reports, and the inclusion of the radiologist's phone number have been proposed, but these solutions have implications for radiologist workflow. Artificial intelligence (AI) has the potential to simplify imaging reports without significant disruptions. Many AI technologies have been applied to radiology reports in the past for various clinical and research purposes, but patient focused solutions have largely been ignored. New natural language processing technologies and large language models (LLMs) have the potential to improve patient understanding of their imaging reports. However, LLMs are a nascent technology and significant research is required before LLM-driven report simplification is used in patient care.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Yale Journal of Biology and Medicine
Yale Journal of Biology and Medicine Biochemistry, Genetics and Molecular Biology-General Biochemistry,Genetics and Molecular Biology
CiteScore
5.00
自引率
0.00%
发文量
41
期刊介绍: The Yale Journal of Biology and Medicine (YJBM) is a graduate and medical student-run, peer-reviewed, open-access journal dedicated to the publication of original research articles, scientific reviews, articles on medical history, personal perspectives on medicine, policy analyses, case reports, and symposia related to biomedical matters. YJBM is published quarterly and aims to publish articles of interest to both physicians and scientists. YJBM is and has been an internationally distributed journal with a long history of landmark articles. Our contributors feature a notable list of philosophers, statesmen, scientists, and physicians, including Ernst Cassirer, Harvey Cushing, Rene Dubos, Edward Kennedy, Donald Seldin, and Jack Strominger. Our Editorial Board consists of students and faculty members from Yale School of Medicine and Yale University Graduate School of Arts & Sciences. All manuscripts submitted to YJBM are first evaluated on the basis of scientific quality, originality, appropriateness, contribution to the field, and style. Suitable manuscripts are then subject to rigorous, fair, and rapid peer review.
期刊最新文献
The Power of Thought: The Role of Psychological Attentiveness and Emotional Support in Patient Trajectories. Bowel Inflammation and Nutrient Supplementation: Effects of a Fixed Combination of Probiotics, Vitamins, and Herbal Extracts in an In Vitro Model of Intestinal Epithelial Barrier Dysfunction. Exploring the Potential of Saffron as a Therapeutic Agent in Depression Treatment: A Comparative Review. Intricate Crosstalk Between Food Allergens, Phages, Bacteria, and Eukaryotic Host Cells of the Gut-skin Axis. Local and Systemic Peptide Therapies for Soft Tissue Regeneration: A Narrative Review.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1