利用人工智能大型语言模型编写皮肤病学临床试验提案:仪器验证研究
Megan Hauptman1, Daniel Copley2, Kelly Young1
1Department of Dermatology, University of Michigan, 1500 E Medical Center Dr, Ann Arbor, MI, 48105, United States, 1 734-936-4054.
JMIR dermatology
|January 12, 2026
概括
大型语言模型 (LLM) 在研究提案开发方面表现有前途,付费的ChatGPT版本是最有能力的. 虽然不取代专家,但LLM可以显著帮助和加快提案过程.
科学领域:
- 医疗保健中的人工智能
- 临床研究操作临床研究操作
- 自然语言处理自然语言处理.
背景情况:
- 大型语言模型 (LLM) 在临床试验设计中越来越多地被采用.
- 它们在研究提案开发中的应用仍然未得到充分利用.
- 本研究评估了LLM在研究提案创建和评估中的有效性.
研究的目的:
- 为了比较开放式大型语言模型 (LLM) 与人类专家在研究提案撰写和审查方面的表现.
- 评估LLM产生的研究建议的准确性和全面性.
主要方法:
- 十个大型语言模型 (LLM) 的任务是生成研究建议.
- 六名医生和LLMs本身评估了11个盲目的研究建议.
- 评估的重点是建议的准确性和全面性.
主要成果:
- 人类得分者认为ChatGPT-o1是最准确的,而Llama 3.1是最不准确的.
- 获得LLM成绩的人认为ChatGPT-o1和DeepSeek R1是最准确的.
- ChatGPT-o1和Llama 3.1分别被人类和LLM得分者评为最全面和最不全面的.
- 总体而言,LLM高估了提案质量,比人类平均高出1.9分.
结论:
- 付费版本的ChatGPT为研究提案任务提供了当前LLM中最高的质量和多功能.
- 法律学可以充当有价值的助手,提高研究开发的效率和生产力.
- 专家的人类投入仍然是不可或缺的,因为LLMs不能完全取代经验丰富的研究人员的细微判断.
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