聊天GPT和Bard的表现在脏病学董事会更新的自我评估问题中
Ryunosuke Noda1, Yuto Izaki2, Fumiya Kitano2
1Division of Nephrology and Hypertension, Department of Internal Medicine, St. Marianna University School of Medicine, 2-16-1 Sugao, Miyamae-Ku, Kawasaki, Kanagawa, 216-8511, Japan. nodaryu00@gmail.com.
Clinical and experimental nephrology
|February 14, 2024
概括
与GPT-3.5和Bard相比,GPT-4在科评估中表现优越,仅略有满足董事会更新标准. 这突显了大型语言模型 (LLM) 在专业医疗领域不断发展的能力和局限性.
科学领域:
- 人工智能在医学中的应用
- 医疗信息学 医疗信息学
- 腎臟病學 (nephrology) 是一種醫學.
背景情况:
- 大型语言模型 (LLM) 在一般医学检查中表现有希望,但它们在脏病学等专业领域的有效性仍未得到评估.
- 这项研究调查了领先的LLM在科特定评估中的表现.
研究的目的:
- 评估ChatGPT (GPT-3.5和GPT-4) 和Bard在科应用中的性能.
- 为了比较LLM的表现与既定科委员会考试标准和居民的表现.
主要方法:
- 使用了九十九个脏病学委员会更新自评问题,从2018年至2022年.
- 聊天GPT (GPT-3.5,GPT-4) 和巴德回答了问题;计算了正确答案率.
- 根据年份和问题类别分析了表现,并与科住院患者进行了比较.
主要成果:
- 总体而言,GPT-4的正确答案率为54.5%,明显超过了GPT-3.5 (31.3%) 和Bard (32.3%).
- 在五年中,GPT-4在三年中达到最低通过门,在解决问题和临床问题方面表现更好.
- GPT-4的表现与第三年和第四年科住院医生的表现差不多.
结论:
- GPT-4在科评估方面表现出潜力,在某些年份仅限于满足董事会更新标准.
- 这些发现强调了当前在专业医疗领域的LLM的能力和局限性.
- 了解LLM的表现对于科医生来说至关重要,因为这些技术不断进步.
相关概念视频
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01:23Renal Clearance
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01:29Assessment of the Gastrointestinal System II: Health Perception Pattern
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01:23Assessment of the Cardiovascular System I: Subjective Data
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121