深度搜索医疗保健:没有伤害吗?
James Anibal1, Steven Bedrick2, Hang Nguyen3
1National Institutes of Health, Bethesda, USA.
概括
医疗保健中的生成人工智能 (AI) 面临着采用障碍. 人工智能模型中支持国家的偏见对医疗保健提供构成风险,正如DeepSeek案例研究所示.
科学领域:
- 医疗保健技术 技术 医疗保健 技术
- 人工智能的人工智能
- 公共卫生 公共卫生
背景情况:
- 医疗保健技术的采用受到可访问性和成本的阻碍.
- 人工智能 (AI) 的进步是显著的,但模型可以表现出偏见.
- 国家行为者可能滥用人工智能来影响公众论是一个令人担忧的问题.
研究的目的:
- 检查"亲国家"偏见对医疗保健服务的潜在影响.
- 为了说明与人工智能知识编辑和有偏见的培训后方法相关的医疗保健风险.
主要方法:
- 使用DeepSeek AI模型进行案例研究分析.
- 检查人工智能后培训方法和知识编辑技术.
主要成果:
- 包括DeepSeek在内的AI模型可能包含"亲国家"偏见.
- 偏见的人工智能可以通过未知的或有缺陷的培训后过程引入医疗保健风险.
- 权力结构滥用人工智能的可能性,以影响公众论.
结论:
- 解决人工智能偏见对于公平的医疗技术采用至关重要.
- 了解AI知识编辑对于减轻医疗保健风险至关重要.
- 生成型人工智能对医疗保健的影响取决于克服可访问性,成本和偏见的挑战.
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