大型语言模型代理可以使用工具来执行临床计算
Alex J Goodell1, Simon N Chu2, Dara Rouholiman3
1Department of Anesthesiology, Pain, and Perioperative Medicine, Stanford University School of Medicine, Stanford, CA, USA. agoodell@stanford.edu.
NPJ digital medicine
|March 18, 2025
概括
大型语言模型 (LLM) 难以处理医学计算. 整合特定任务的工具显著提高了准确性,显示了更安全的临床AI应用的潜力.
科学领域:
- 人工智能在医学中的应用
- 临床决策支持系统 临床决策支持系统
- 计算医学是一种计算医学.
背景情况:
- 大型语言模型 (LLM) 证明了在医疗问题解答方面的专业知识.
- 在LLM中,存在显著的局限性,包括幻觉和算术错误.
- 目前的LLM在临床计算方面的表现阻碍了整合到医疗保健工作流程中.
研究的目的:
- 为了评估ChatGPT在执行医学计算中的准确性.
- 评估代理增强策略对医学计算中的LLM绩效的影响.
- 确定特定任务工具在提高临床计算LLM可靠性的有效性.
主要方法:
- 在48个不同的医疗计算任务中评估了ChatGPT的性能.
- 测试了三个增强方法:检索增强生成,代码解释器和特定任务工具 (OpenMedCalc).
- 进行了超过1万项试验,以评估带有和没有增强的模型性能.
主要成果:
- 在三分之一的初步试验中,ChatGPT产生了不正确的反应.
- 用特定任务工具增强的模型显示了显著的改进.
- 使用工具的基于LLaMa和GPT的模型减少了5.5倍和13倍的错误反应.
结论:
- 法律学士面临医学算术的挑战,影响临床实用性.
- 特定任务,机器可读的工具是有效的减轻LLM计算错误.
- 整合专业工具可以提高临床应用LLM的可靠性.
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