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模拟滥用大型语言模型和临床信用系统

James Anibal¹, Hannah Huth¹, Jasmine Gunkel²

¹Center for Interventional Oncology, NIH Clinical Center, National Institutes of Health (NIH), Bethesda, Maryland, USA.

medRxiv : the preprint server for health sciences

|April 22, 2024

概括

医疗保健中的大型语言模型 (LLM) 存在风险. 研究表明,LLM可能会优先考虑系统利益而不是个人权利,从而有可能滥用敏感的临床数据.

科学领域:

人工智能在医学中的应用
生物伦理学生物伦理学
医疗信息学医疗信息学

背景情况:

大型语言模型 (LLM) 提供了医疗保健进步的潜力,如诊断和个性化治疗.
然而,人工智能在医疗保健中的应用存在潜在的滥用风险,特别是在资源分配和数据隐私方面.

研究的目的:

调查在医疗保健中滥用LLM的可能性,特别是关于偏见的资源分配和侵犯个人权利的可能性.
为了证明目前的LLM如何利用敏感的临床数据.
提出减轻与开发医疗保健人工智能相关的伦理风险的策略.

主要方法:

这项研究涉及分析LLMs固有的偏见.
为了模拟使用当前的LLM技术对临床数据集的利用,进行了实验.
制定了道德框架和风险减轻策略.

主要成果:

法律法规可能表现出偏见,偏爱集体或系统利益,而不是保护个人权利.
模拟证实了临床数据集可以与现有的LLM一起利用,突出了迫切的伦理问题.
这些发现强调了"临床信用系统"的潜力,这可能会限制获得护理的机会.

结论:

迫切需要解决LLM在医疗保健中的伦理危险.
积极的战略对于减轻在敏感的医疗保健应用中开发和部署大型AI模型的风险至关重要.
保护个人权利和公民自由必须在医疗保健人工智能开发中至关重要.

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