在放射性瘤学中探索大型语言模型 (如ChatGPT) 的能力
Fabio Dennstädt1, Janna Hastings2,3, Paul Martin Putora1,4
1Department of Radiation Oncology, Kantonsspital St. Gallen, St. Gallen, Switzerland.
Advances in radiation oncology
|February 2, 2024
概括
像ChatGPT这样的大型语言模型在回答放射治疗问题方面表现有前途,在多选项问题上达到高准确度,在开放式查询中达到可接受的质量. 然而,由于它们目前在始终提供正确医疗信息方面的局限性,在临床应用中需要谨慎.
科学领域:
- 人工智能在医学中的应用
- 自然语言处理应用程序
- 辐射瘤学知识评估知识评估
背景情况:
- 机器学习和NLP的进步使复杂的大型语言模型 (LLM) 成为可能.
- 交谈式LLM,如ChatGPT,在包括专业医疗领域在内的各种领域展示了显著的能力.
- 在评估医学知识,特别是辐射疗法中,LLM的潜在应用值得研究.
研究的目的:
- 探索ChatGPT在回答与放射治疗有关的问题的能力.
- 在专业医疗环境中评估LLM产生的反应的准确性和质量.
主要方法:
- 开发了一套多选项和开放式问题,涵盖放射性瘤学的临床,物理和生物学方面.
- 聊天GPT被要求提出这些问题,并收集了其回复.
- 多选项答案的正确性被评估,而开放式答案的正确性和有用性被辐射瘤学家评估在利克尔特尺度上.
主要成果:
- 聊天GPT为94.3%的多选题提供了有效答案,整体正确答案率为60.61% (根据子专业而异).
- 对于开放式问题,25个答案中的12个被所有评价者评为可接受,好或非常好.
- 评估人员发现,在28-29.3%的案例中,ChatGPT的回应是"非常好的",在28-29.3%的案例中,关于正确性和帮助性的回应是"好的".
结论:
- 聊天GPT可以对许多放射治疗问题产生令人满意的反应,这表明其潜在的实用性.
- 目前LLM无法始终提供准确的医疗信息,这使得它们直接用于医疗查询具有问题.
- 预计未来的LLM改进将增加其对临床实践的影响,包括辐射瘤学.
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