在骨科手术中使用ChatGPT进行临床决策支持的挑战:一项试点研究。

IF 2.6 2区 医学 Q1 ORTHOPEDICS
Michael A McNamara, Brandon G Hill, Peter L Schilling
{"title":"在骨科手术中使用ChatGPT进行临床决策支持的挑战:一项试点研究。","authors":"Michael A McNamara, Brandon G Hill, Peter L Schilling","doi":"10.5435/JAAOS-D-24-01072","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Artificial intelligence (AI) technologies have recently exploded in both accessibility and applicability, including in health care. Although studies have demonstrated its ability to adequately answer simple patient issues or multiple-choice questions, its capacity for deeper complex decision making within health care is relatively untested. In this study, we aimed to delve into AI's ability to integrate multiple clinical data sources and produce a reasonable assessment and plan, specifically in the setting of an orthopaedic surgery consultant.</p><p><strong>Methods: </strong>Ten common fractures seen by orthopaedic surgeons in the emergency department were chosen. Consult notes from patients sustaining each of these fractures, seen at a level 1 academic trauma center between 2022 and 2023, were stripped of patient data. The history, physical examination, and imaging interpretations were then given to ChatGPT4 in raw and semistructured formats. The AI was asked to determine an assessment and plan as if it were an orthopaedic surgeon. The generated plans were then compared with the actual clinical course of the patient, as determined by our multispecialty trauma conference.</p><p><strong>Results: </strong>When given both raw and semistructured formats of clinical data, ChatGPT4 determined safe and reasonable plans that included the final clinical outcome of the patient scenario. Evaluating large language models is an ongoing field of research without an established quantitative rubric; therefore, our conclusions rely on subjective comparison.</p><p><strong>Conclusion: </strong>When given history, physical examination, and imaging interpretations, ChatGPT is able to synthesize complex clinical data into a reasonable and most importantly safe assessment and plan for common fractures seen by orthopaedic surgeons. Evaluating large language models is an ongoing challenge; however, using actual clinical courses as a \"benchmark\" for comparison presents a possible avenue for further research.</p>","PeriodicalId":51098,"journal":{"name":"Journal of the American Academy of Orthopaedic Surgeons","volume":" ","pages":""},"PeriodicalIF":2.6000,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"The Challenges of Using ChatGPT for Clinical Decision Support in Orthopaedic Surgery: A Pilot Study.\",\"authors\":\"Michael A McNamara, Brandon G Hill, Peter L Schilling\",\"doi\":\"10.5435/JAAOS-D-24-01072\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>Artificial intelligence (AI) technologies have recently exploded in both accessibility and applicability, including in health care. Although studies have demonstrated its ability to adequately answer simple patient issues or multiple-choice questions, its capacity for deeper complex decision making within health care is relatively untested. In this study, we aimed to delve into AI's ability to integrate multiple clinical data sources and produce a reasonable assessment and plan, specifically in the setting of an orthopaedic surgery consultant.</p><p><strong>Methods: </strong>Ten common fractures seen by orthopaedic surgeons in the emergency department were chosen. Consult notes from patients sustaining each of these fractures, seen at a level 1 academic trauma center between 2022 and 2023, were stripped of patient data. The history, physical examination, and imaging interpretations were then given to ChatGPT4 in raw and semistructured formats. The AI was asked to determine an assessment and plan as if it were an orthopaedic surgeon. The generated plans were then compared with the actual clinical course of the patient, as determined by our multispecialty trauma conference.</p><p><strong>Results: </strong>When given both raw and semistructured formats of clinical data, ChatGPT4 determined safe and reasonable plans that included the final clinical outcome of the patient scenario. Evaluating large language models is an ongoing field of research without an established quantitative rubric; therefore, our conclusions rely on subjective comparison.</p><p><strong>Conclusion: </strong>When given history, physical examination, and imaging interpretations, ChatGPT is able to synthesize complex clinical data into a reasonable and most importantly safe assessment and plan for common fractures seen by orthopaedic surgeons. Evaluating large language models is an ongoing challenge; however, using actual clinical courses as a \\\"benchmark\\\" for comparison presents a possible avenue for further research.</p>\",\"PeriodicalId\":51098,\"journal\":{\"name\":\"Journal of the American Academy of Orthopaedic Surgeons\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":2.6000,\"publicationDate\":\"2025-03-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of the American Academy of Orthopaedic Surgeons\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.5435/JAAOS-D-24-01072\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ORTHOPEDICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of the American Academy of Orthopaedic Surgeons","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.5435/JAAOS-D-24-01072","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ORTHOPEDICS","Score":null,"Total":0}
引用次数: 0

摘要

背景:人工智能(AI)技术最近在可及性和适用性方面都出现了爆炸式增长,包括在医疗保健领域。虽然研究已经证明它有能力充分回答简单的病人问题或多项选择题,但它在医疗保健领域进行更深层复杂决策的能力相对来说还未经检验。在这项研究中,我们旨在深入研究人工智能整合多个临床数据源并产生合理评估和计划的能力,特别是在骨科手术顾问的设置中。方法:选取急诊骨科常见骨折病例10例。在2022年至2023年期间,在一级学术创伤中心看到的每一例骨折患者的咨询记录都被剥夺了患者数据。然后以原始和半结构化格式向ChatGPT4提供病史、体格检查和影像学解释。人工智能被要求像整形外科医生一样确定评估和计划。然后将生成的计划与患者的实际临床病程进行比较,由我们的多专业创伤会议确定。结果:当给出原始和半结构化的临床数据格式时,ChatGPT4确定了安全合理的计划,包括患者情景的最终临床结果。评估大型语言模型是一个正在进行的研究领域,没有既定的定量准则;因此,我们的结论依赖于主观比较。结论:在给定病史、体格检查和影像学解释后,ChatGPT能够将复杂的临床数据综合为骨科医生常见骨折的合理且最重要的是安全的评估和计划。评估大型语言模型是一个持续的挑战;然而,使用实际临床课程作为“基准”进行比较,为进一步研究提供了可能的途径。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The Challenges of Using ChatGPT for Clinical Decision Support in Orthopaedic Surgery: A Pilot Study.

Background: Artificial intelligence (AI) technologies have recently exploded in both accessibility and applicability, including in health care. Although studies have demonstrated its ability to adequately answer simple patient issues or multiple-choice questions, its capacity for deeper complex decision making within health care is relatively untested. In this study, we aimed to delve into AI's ability to integrate multiple clinical data sources and produce a reasonable assessment and plan, specifically in the setting of an orthopaedic surgery consultant.

Methods: Ten common fractures seen by orthopaedic surgeons in the emergency department were chosen. Consult notes from patients sustaining each of these fractures, seen at a level 1 academic trauma center between 2022 and 2023, were stripped of patient data. The history, physical examination, and imaging interpretations were then given to ChatGPT4 in raw and semistructured formats. The AI was asked to determine an assessment and plan as if it were an orthopaedic surgeon. The generated plans were then compared with the actual clinical course of the patient, as determined by our multispecialty trauma conference.

Results: When given both raw and semistructured formats of clinical data, ChatGPT4 determined safe and reasonable plans that included the final clinical outcome of the patient scenario. Evaluating large language models is an ongoing field of research without an established quantitative rubric; therefore, our conclusions rely on subjective comparison.

Conclusion: When given history, physical examination, and imaging interpretations, ChatGPT is able to synthesize complex clinical data into a reasonable and most importantly safe assessment and plan for common fractures seen by orthopaedic surgeons. Evaluating large language models is an ongoing challenge; however, using actual clinical courses as a "benchmark" for comparison presents a possible avenue for further research.

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
CiteScore
6.10
自引率
6.20%
发文量
529
审稿时长
4-8 weeks
期刊介绍: The Journal of the American Academy of Orthopaedic Surgeons was established in the fall of 1993 by the Academy in response to its membership’s demand for a clinical review journal. Two issues were published the first year, followed by six issues yearly from 1994 through 2004. In September 2005, JAAOS began publishing monthly issues. Each issue includes richly illustrated peer-reviewed articles focused on clinical diagnosis and management. Special features in each issue provide commentary on developments in pharmacotherapeutics, materials and techniques, and computer applications.
×
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
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
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 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信