使用大型语言模型评估放射科住院医生的报告技巧:一项观察性研究
Natsuko Atsukawa1, Hiroyuki Tatekawa2, Tatsushi Oura1
1Department of Diagnostic and Interventional Radiology, Graduate School of Medicine, Osaka Metropolitan University, 1-4-3, Asahi-Machi, Abeno-ku, Osaka, 545-8585, Japan.
Japanese journal of radiology
|March 8, 2025
概括
大型语言模型 (LLM) 可以客观地评估放射学报告,帮助居民技能发展. GPT-4o显示了与放射科医生的最高一致性,改善了对常见报告错误的反.
科学领域:
- 放射学中的人工智能
- 医疗教育 技术 技术 医学教育
- 放射学居民培训课程 放射学居民培训课程
背景情况:
- 关于在放射学培训中使用大语言模型 (LLM) 进行反的研究有限.
- 通过LLM评估的报告来评估居民技能发展是一个新兴的领域.
- 通过LLM对放射学住院医生报告的客观评估具有显著的潜力.
研究的目的:
- 评估LLMs在修订放射学报告中的有效性.
- 将LLM修订的报告与经过董事会认证的放射科医生核实的报告进行比较.
- 通过LLM反,分析居民报告技能随时间的进展.
主要方法:
- 由三个LLM (GPT-4o,克劳德-3.5索内特,克劳德-3 Opus) 评估了九名第一年住院医生的100份放射学报告.
- 报告根据六个标准进行评估:发现的添加/删除,表达的纠正,诊断的纠正,以及额外的检查/治疗.
- 对比了900份来自第一和最后一个居住期的报告,以分析技能进步.
主要成果:
- 在测试的LLMs中,GPT-4o显示了与人类放射学家的最高一致性.
- 当使用GPT-4o时,在添加缺失的积极发现,删除不正确的发现和添加负面发现 (P < .001到P = .004) 时观察到显著的改善.
- 虽然第4至6标准没有显著变化,但除第6标准外,所有标准都随着时间的推移呈现出逐步增强.
结论:
- 实际上,LLM有效地提供了对放射学报告经常纠正的方面的反.
- 居民可以客观地识别和改善弱点,并通过LLM反来跟踪进展.
- 实际上,LLM可能会降低放射科医生和导师的工作量.
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