在不同临床场景中对抗生素处方进行大型语言模型的比较:哪一个表现更好?
Andrea De Vito1, Nicholas Geremia2, Davide Fiore Bavaro3
1Unit of Infectious Diseases, Department of Medicine, Surgery and Pharmacy, Sassari, Italy.
在评估的大型语言模型 (LLM) 中,ChatGPT-o1在抗生素处方方面表现出卓越的准确性. 虽然对临床决策支持有希望,但LLM的性能各不相同,特别是在复杂的情况下,在广泛使用之前需要仔细验证.
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
- 抗微生物药物管理委员会
背景情况:
- 大型语言模型 (LLM) 为临床决策提供了潜力.
- 有限的比较数据存在于抗生素处方的LLM准确性.
- 评估LLM绩效对于安全的临床整合至关重要.
研究的目的:
- 评估各种LLM的抗生素处方准确性.
- 在不同的临床场景和感染类型中比较LLM的表现.
- 为抗生素决策支持确定领先的LLM.
主要方法:
- 14个LLM被评估使用60个不同的临床病例与antibiograms.
- 一个标准化的提示指导药物选择,剂量和持续时间的建议.
- 答案是匿名的,并由一个盲目的专家小组进行评估.
主要成果:
- ChatGPT-o1获得了最高的抗生素处方准确率 (71.7%).
- 对ChatGPT-o1 (96.7%) 的剂量正确性最高.
- 双子座显示最合适的治疗持续时间建议 (75.0%),而克劳德3.5索内特倾向于过度处方.
结论:
- 在LLM抗生素处方能力中存在显著的变化.
- ChatGPT-o1显示出作为抗生素选择临床决策支持工具的潜力.
- 复杂病例中的精度降低需要在临床应用之前进行彻底的验证.
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