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2023年02月20日 15時11分 ID:aa08ddc2f1 （1/2） ID抽出返信

ChatGPTと呼ばれる大規模言語モデルの性能を、米国の医師資格試験USMLE（United States Medical Licensing Exam）において評価しました。ChatGPTは、特別な訓練や強化なしに、3つの試験すべてにおいて合格基準値かそれに近い結果を出しました。また、ChatGPTは、説明の一致度や洞察力が高いことを実証しました。これらの結果は、大規模言語モデルが医学教育や、潜在的には臨床的な意思決定を支援する可能性があることを示唆しています。
https://journals.plos.org/digitalhealth/article?id=10.1371/journal.pdig.0000198

Performance of ChatGPT on USMLE: Potential for AI-assisted medical education using large language models
https://journals.plos.org/digitalhealth/article?id=10.1371/journal.pdig.0000198
Author summary Artificial intelligence (AI) systems hold great promise to improve medical care and health outcomes. As such, it is crucial to ensure that the development of clinical AI is guided by the principles of trust and explainability. Measuring AI medical knowledge in comparison to that of expert human clinicians is a critical first step in evaluating these qualities. To accomplish this, we evaluated the performance of ChatGPT, a language-based AI, on the United States Medical Licensing Exam (USMLE). The USMLE is a set of three standardized tests of expert-level knowledge, which are required for medical licensure in the United States. We found that ChatGPT performed at or near the passing threshold of 60% accuracy. Being the first to achieve this benchmark, this marks a notable milestone in AI maturation. Impressively, ChatGPT was able to achieve this result without specialized input from human trainers. Furthermore, ChatGPT displayed comprehensible reasoning and valid clinical insights, lending increased confidence to trust and explainability. Our study suggests that large language models such as ChatGPT may potentially assist human learners in a medical education setting, as a prelude to future integration into clinical decision-making.

Performance of ChatGPT on USMLE: Potential for AI-assisted medical education using large language models
https://journals.plos.org/digitalhealth/article?id=10.1371/journal.pdig.0000198

Author summary Artificial intelligence (AI) systems hold great promise to improve medical care and health outcomes. As such, it is crucial to ensure that the development of clinical AI is guided by the principles of trust and explainability. Measuring AI medical knowledge in comparison to that of expert human clinicians is a critical first step in evaluating these qualities. To accomplish this, we evaluated the performance of ChatGPT, a language-based AI, on the United States Medical Licensing Exam (USMLE). The USMLE is a set of three standardized tests of expert-level knowledge, which are required for medical licensure in the United States. We found that ChatGPT performed at or near the passing threshold of 60% accuracy. Being the first to achieve this benchmark, this marks a notable milestone in AI maturation. Impressively, ChatGPT was able to achieve this result without specialized input from human trainers. Furthermore, ChatGPT displayed comprehensible reasoning and valid clinical insights, lending increased confidence to trust and explainability. Our study suggests that large language models such as ChatGPT may potentially assist human learners in a medical education setting, as a prelude to future integration into clinical decision-making.

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