抗生素和人工智能:对快速发展的景观的临床考虑
Daniele Roberto Giacobbe1,2, Sabrina Guastavino3, Cristina Marelli4,5
1UO Clinica Malattie Infettive, IRCCS Ospedale Policlinico San Martino, L.Go R. Benzi, 10, 16132, Genoa, Italy. danieleroberto.giacobbe@unige.it.
Infectious diseases and therapy
|February 15, 2025
概括
人工智能 (AI) 和大型语言模型 (LLM) 显示出对抗生素处方的希望. 然而,在它们的现实应用中存在挑战,包括概念差异,专业知识悖论和错误风险.
科学领域:
- 医疗保健 人工智能 医疗保健 人工智能
- 临床决策支持系统 临床决策支持系统
- 抗生素管理局 抗生素管理局
背景情况:
- 人工智能 (AI) 对医疗保健决策越来越感兴趣,特别是在抗生素处方方面.
- 大型语言模型 (LLM) 提供了处理和生成基于文本的临床信息的潜力.
- 实施基于LLM的抗生素处方支持具有独特的复杂性.
研究的目的:
- 探索LLM在抗生素处方中的应用.
- 为了区分LLM在科学写作与临床实践中的使用.
- 检查与该领域的LLM相关的专业知识悖论和错误风险.
主要方法:
- 在医疗保健LLM应用的概念分析.
- 在科学写作和临床决策支持中使用LLM之间的共同点和差异的讨论.
- 探索LLM辅助抗生素处方中的专业知识悖论和错误风险.
主要成果:
- 确定了科学写作和现实世界抗生素处方之间的LLM应用中的关键概念差异.
- 讨论了临床医生与LLMs互动时的专业知识悖论的细微差别.
- 突出了LLM支持的复杂任务中的特定特点和错误风险,例如抗生素选择.
结论:
- 与科学写作相比,对抗生素处方的LLM实施需要仔细考虑不同的挑战.
- 解决专业知识悖论和减轻错误风险对于安全有效的LLM集成至关重要.
- 需要进一步的研究,以了解在传染病管理中基于LLM的临床决策支持的复杂性.
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