Exploring the potential of ChatGPT as a supplementary tool for providing orthopaedic information

医学 骨科手术 适应性 正确性 前交叉韧带 比例(比率) 运动医学 物理疗法 医学教育 计算机科学 外科 生态学 物理 量子力学 生物 程序设计语言
作者
Janina Kaarre,Robert Feldt,Laura E. Keeling,Sahil Dadoo,Bálint Zsidai,Jonathan D. Hughes,Kristian Samuelsson,Volker Musahl
出处
期刊:Knee Surgery, Sports Traumatology, Arthroscopy [Springer Nature]
标识
DOI:10.1007/s00167-023-07529-2
摘要

Abstract Purpose To investigate the potential use of large language models (LLMs) in orthopaedics by presenting queries pertinent to anterior cruciate ligament (ACL) surgery to generative pre-trained transformer (ChatGPT, specifically using its GPT-4 model of March 14th 2023). Additionally, this study aimed to evaluate the depth of the LLM’s knowledge and investigate its adaptability to different user groups. It was hypothesized that the ChatGPT would be able to adapt to different target groups due to its strong language understanding and processing capabilities. Methods ChatGPT was presented with 20 questions and response was requested for two distinct target audiences: patients and non-orthopaedic medical doctors. Two board-certified orthopaedic sports medicine surgeons and two expert orthopaedic sports medicine surgeons independently evaluated the responses generated by ChatGPT. Mean correctness, completeness, and adaptability to the target audiences (patients and non-orthopaedic medical doctors) were determined. A three-point response scale facilitated nuanced assessment. Results ChatGPT exhibited fair accuracy, with average correctness scores of 1.69 and 1.66 (on a scale from 0, incorrect, 1, partially correct, to 2, correct) for patients and medical doctors, respectively. Three of the 20 questions (15.0%) were deemed incorrect by any of the four orthopaedic sports medicine surgeon assessors. Moreover, overall completeness was calculated to be 1.51 and 1.64 for patients and medical doctors, respectively, while overall adaptiveness was determined to be 1.75 and 1.73 for patients and doctors, respectively. Conclusion Overall, ChatGPT was successful in generating correct responses in approximately 65% of the cases related to ACL surgery. The findings of this study imply that LLMs offer potential as a supplementary tool for acquiring orthopaedic knowledge. However, although ChatGPT can provide guidance and effectively adapt to diverse target audiences, it cannot supplant the expertise of orthopaedic sports medicine surgeons in diagnostic and treatment planning endeavours due to its limited understanding of orthopaedic domains and its potential for erroneous responses. Level of evidence V.
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