亲爱的研友该休息了!由于当前在线用户较少,发布求助请尽量完整地填写文献信息,科研通机器人24小时在线,伴您度过漫漫科研夜!身体可是革命的本钱,早点休息,好梦!

Performance of ChatGPT on a Radiology Board-style Examination: Insights into Current Strengths and Limitations

医学 风格(视觉艺术) 召回 订单(交换) 放射科 医学物理学 认知心理学 心理学 财务 历史 经济 考古
作者
Rajesh Bhayana,Satheesh Krishna,Robert R. Bleakney
出处
期刊:Radiology [Radiological Society of North America]
卷期号:307 (5) 被引量:328
标识
DOI:10.1148/radiol.230582
摘要

Background ChatGPT is a powerful artificial intelligence large language model with great potential as a tool in medical practice and education, but its performance in radiology remains unclear. Purpose To assess the performance of ChatGPT on radiology board-style examination questions without images and to explore its strengths and limitations. Materials and Methods In this exploratory prospective study performed from February 25 to March 3, 2023, 150 multiple-choice questions designed to match the style, content, and difficulty of the Canadian Royal College and American Board of Radiology examinations were grouped by question type (lower-order [recall, understanding] and higher-order [apply, analyze, synthesize] thinking) and topic (physics, clinical). The higher-order thinking questions were further subclassified by type (description of imaging findings, clinical management, application of concepts, calculation and classification, disease associations). ChatGPT performance was evaluated overall, by question type, and by topic. Confidence of language in responses was assessed. Univariable analysis was performed. Results ChatGPT answered 69% of questions correctly (104 of 150). The model performed better on questions requiring lower-order thinking (84%, 51 of 61) than on those requiring higher-order thinking (60%, 53 of 89) (P = .002). When compared with lower-order questions, the model performed worse on questions involving description of imaging findings (61%, 28 of 46; P = .04), calculation and classification (25%, two of eight; P = .01), and application of concepts (30%, three of 10; P = .01). ChatGPT performed as well on higher-order clinical management questions (89%, 16 of 18) as on lower-order questions (P = .88). It performed worse on physics questions (40%, six of 15) than on clinical questions (73%, 98 of 135) (P = .02). ChatGPT used confident language consistently, even when incorrect (100%, 46 of 46). Conclusion Despite no radiology-specific pretraining, ChatGPT nearly passed a radiology board-style examination without images; it performed well on lower-order thinking questions and clinical management questions but struggled with higher-order thinking questions involving description of imaging findings, calculation and classification, and application of concepts. © RSNA, 2023 See also the editorial by Lourenco et al and the article by Bhayana et al in this issue.

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
6秒前
白华苍松发布了新的文献求助10
11秒前
YNYang完成签到,获得积分10
16秒前
20秒前
chbsad123发布了新的文献求助10
25秒前
27秒前
33秒前
1分钟前
yshj完成签到 ,获得积分10
1分钟前
NexusExplorer应助怡然远望采纳,获得10
1分钟前
打打应助科研通管家采纳,获得10
1分钟前
1分钟前
1分钟前
wzhnb发布了新的文献求助10
2分钟前
2分钟前
2分钟前
白华苍松发布了新的文献求助10
2分钟前
2分钟前
2分钟前
懒回顾发布了新的文献求助10
2分钟前
2分钟前
懒回顾完成签到,获得积分10
2分钟前
3分钟前
忧郁丹彤完成签到,获得积分10
3分钟前
ZYP完成签到,获得积分10
3分钟前
3分钟前
3分钟前
忧郁丹彤发布了新的文献求助10
3分钟前
3分钟前
3分钟前
金沐栋完成签到,获得积分10
3分钟前
3分钟前
白华苍松发布了新的文献求助10
3分钟前
4分钟前
4分钟前
无极微光应助明理丹烟采纳,获得40
4分钟前
4分钟前
4分钟前
4分钟前
白华苍松发布了新的文献求助10
4分钟前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
Predation in the Hymenoptera: An Evolutionary Perspective 1800
List of 1,091 Public Pension Profiles by Region 1561
Binary Alloy Phase Diagrams, 2nd Edition 1200
Holistic Discourse Analysis 600
Beyond the sentence: discourse and sentential form / edited by Jessica R. Wirth 600
Atlas of Liver Pathology: A Pattern-Based Approach 500
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 纳米技术 计算机科学 内科学 化学工程 复合材料 物理化学 基因 遗传学 催化作用 冶金 量子力学 光电子学
热门帖子
关注 科研通微信公众号,转发送积分 5509696
求助须知:如何正确求助?哪些是违规求助? 4604500
关于积分的说明 14489844
捐赠科研通 4539312
什么是DOI,文献DOI怎么找? 2487475
邀请新用户注册赠送积分活动 1469865
关于科研通互助平台的介绍 1442088