传染病专家与ChatGPT®之间的建议抗生素治疗协议
Santiago Montiel-Romero1, Sandra Rajme-López1, Carla Marina Román-Montes1,2
1Department of Infectious Diseases, Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Mexico City, Mexico, 15 Vasco de Quiroga, Belisario Domínguez Secc 16 Tlalpan, Mexico City, 14080.
人工智能工具ChatGPT®在推模拟病例的抗生素处方方面与传染病 (ID) 专家达成不良协议. 在抗微生物药物管理中临床使用之前需要进一步验证.
科学领域:
- 医疗信息学 医疗信息学
- 传染性疾病 传染性疾病
- 医疗保健中的人工智能
背景情况:
- 抗微生物药物耐药性构成了全球重大公共卫生挑战.
- 像ChatGPT®这样的人工智能 (AI) 语言模型提供了实时分析抗菌素敏感性数据的潜力.
- 在资源有限,缺乏传染病专家的环境中,人工智能工具可能特别有价值.
研究的目的:
- 评估ChatGPT®和ID专家之间的协议,以确定适当的抗生素处方.
- 评估ChatGPT®在识别抗菌素耐药性机制方面的准确性.
主要方法:
- 创建了100个模拟患者病例,提供详细的临床信息和抗生素图.
- 病例由ChatGPT®和五名ID专家使用特定指令进行分析.
- 对抗生素选择和耐药性机制的共识是使用科恩的卡帕系数计算的.
主要成果:
- 在51%的病例中 (kappa=0.48) 观察到对适当的抗生素处方达成一致.
- 在42%的病例中 (kappa=0.39) 发现了对抗生素耐药性机制的一致性.
- 在不同的传染性综合征和微生物中,协议有很大差异.
结论:
- 目前,ChatGPT®和ID专家之间对于模拟场景中的抗生素管理建议达成的协议较差.
- 这些发现表明ChatGPT®在传染病实时临床决策支持方面的可靠性有限.
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