Advancements in large language model accuracy for answering physical medicine and rehabilitation board review questions.

IF 2.2 4区 医学 Q1 REHABILITATION
PM&R Pub Date : 2025-05-02 DOI:10.1002/pmrj.13386
Jason Bitterman, Alexander D'Angelo, Alexandra Holachek, James E Eubanks
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引用次数: 0

Abstract

Background: There have been significant advances in machine learning and artificial intelligence technology over the past few years, leading to the release of large language models (LLMs) such as ChatGPT. There are many potential applications for LLMs in health care, but it is critical to first determine how accurate LLMs are before putting them into practice. No studies have evaluated the accuracy and precision of LLMs in responding to questions related to the field of physical medicine and rehabilitation (PM&R).

Objective: To determine the accuracy and precision of two OpenAI LLMs (GPT-3.5, released in November 2022, and GPT-4o, released in May 2024) in answering questions related to PM&R knowledge.

Design: Cross-sectional study. Both LLMs were tested on the same 744 PM&R knowledge questions that covered all aspects of the field (general rehabilitation, stroke, traumatic brain injury, spinal cord injury, musculoskeletal medicine, pain medicine, electrodiagnostic medicine, pediatric rehabilitation, prosthetics and orthotics, rheumatology, and pharmacology). Each LLM was tested three times on the same question set to assess for precision.

Setting: N/A.

Patients: N/A.

Interventions: N/A.

Main outcome measure: Percentage of correctly answered questions.

Results: For three runs of the 744-question set, GPT-3.5 answered 56.3%, 56.5%, and 56.9% of the questions correctly. For three runs of the same question set, GPT-4o answered 83.6%, 84%, and 84.1% of the questions correctly. GPT-4o outperformed GPT-3.5 in all subcategories of PM&R questions.

Conclusions: LLM technology is rapidly advancing, with the more recent GPT-4o model performing much better on PM&R knowledge questions compared to GPT-3.5. There is potential for LLMs in augmenting clinical practice, medical training, and patient education. However, the technology has limitations and physicians should remain cautious in using it in practice at this time.

回答物理医学和康复委员会审查问题的大语言模型准确性的进展。
背景:在过去几年中,机器学习和人工智能技术取得了重大进展,导致诸如ChatGPT之类的大型语言模型(llm)的发布。llm在医疗保健领域有许多潜在的应用,但在付诸实践之前,首先确定llm的准确性是至关重要的。没有研究评估法学硕士在回答与物理医学和康复(PM&R)领域相关的问题时的准确性和精确性。目的:确定两种OpenAI LLMs(2022年11月发布的GPT-3.5和2024年5月发布的gpt - 40)在回答PM&R知识相关问题中的准确性和精密度。设计:横断面研究。两位法学硕士都接受了相同的744个PM&R知识问题的测试,这些问题涵盖了该领域的各个方面(一般康复、中风、创伤性脑损伤、脊髓损伤、肌肉骨骼医学、疼痛医学、电诊断医学、儿科康复、假肢和矫形学、风湿病学和药理学)。每个LLM在相同的问题集上测试了三次,以评估准确性。设置:N / A。病人:N / A。干预措施:N / A。主要结果测量:正确回答问题的百分比。结果:对于744个问题集的三次运行,GPT-3.5正确回答了56.3%,56.5%和56.9%的问题。对于同一个问题集的三次测试,gpt - 40答对了83.6%、84%和84.1%的问题。gpt - 40在PM&R问题的所有子类别中都优于GPT-3.5。结论:法学硕士技术正在迅速发展,与GPT-3.5相比,最新的gpt - 40模型在PM&R知识问题上表现得更好。法学硕士在增加临床实践、医学培训和患者教育方面具有潜力。然而,该技术有局限性,医生在实际应用时应保持谨慎。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
PM&R
PM&R REHABILITATION-SPORT SCIENCES
CiteScore
4.30
自引率
4.80%
发文量
187
审稿时长
4-8 weeks
期刊介绍: Topics covered include acute and chronic musculoskeletal disorders and pain, neurologic conditions involving the central and peripheral nervous systems, rehabilitation of impairments associated with disabilities in adults and children, and neurophysiology and electrodiagnosis. PM&R emphasizes principles of injury, function, and rehabilitation, and is designed to be relevant to practitioners and researchers in a variety of medical and surgical specialties and rehabilitation disciplines including allied health.
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