大型语言模型对健康应用的未来作用取决于监管机构执行安全标准
Oscar Freyer1, Isabella Catharina Wiest2, Jakob Nikolas Kather3
1Else Kröner Fresenius Center for Digital Health, TUD Dresden University of Technology, Dresden, Germany.
The Lancet. Digital health
|August 23, 2024
概括
大型语言模型 (LLM) 提供医疗保健潜力,但由于人工智能幻觉等安全问题,面临监管障碍. 迫切需要框架和执法,以确保医疗领域的安全AI.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 监管科学 监管科学
背景情况:
- 大型语言模型 (LLM),包括像生成预训练变压器-4这样的高级版本,越来越多地集成到临床环境中.
- 这些人工智能工具在医疗保健,诊断和患者护理方面表现有前途,但它们的部署引发了重大安全和监管问题.
研究的目的:
- 检查与在医疗保健中使用LLM相关的监管挑战和安全问题.
- 强调需要新的监管框架和对人工智能驱动的医疗应用实施现有法规.
主要方法:
- 对美国和欧盟当前有关AI医疗设备的监管格局的分析.
- 审查LLM固有的局限性,如输出可变性,缺乏可解释性和AI幻觉.
- 检查基于LLM的医疗保健应用程序的市场可用性,尽管存在未解决的风险.
主要成果:
- 基于LLM的医疗保健应用面临着重大监管挑战,因为医疗器械的可变性,可解释性和幻觉风险.
- 现有的法规难以适应LLM的独特性质,导致监管模两可.
- 尽管存在错误诊断和未经验证的建议等风险,但这些应用程序目前可供公众使用.
结论:
- 迫切需要针对医疗保健中LLM的能力和局限性的监管框架.
- 在制定新框架的同时,执行现有法规至关重要.
- 如果不解决这些问题,可能会伤害患者,并削弱LLM获得医疗建议的潜力.
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