临床数据分析中的人工智能:大型语言模型,基础模型,数字双胞胎和过敏应用的审查
Yutaro Fuse1, Shawn N Murphy2, Hisahiro Ikari1
1Department of Artificial Intelligence Medicine, Graduate School of Medicine, Chiba University, Chiba, Japan.
概括
人工智能正在改变临床研究和医疗保健, 这篇评论详细介绍了分析临床数据的机器学习方法,从目前的技术到未来的方向,比如数字双胞胎.
科学领域:
- 临床医学
- 人工智能
- 数据科学
背景情况:
- 计算和数据利用的进步加速了医疗保健中的人工智能应用.
- 机器学习对于分析复杂的临床数据越来越重要.
- 过敏疾病为人工智能带来的见解带来独特的挑战和机遇.
研究的目的:
- 为临床数据分析提供当前ML技术的全面概述.
- 突出新兴的人工智能方法,包括医疗图像分析和EHR时间序列建模.
- 探索人工智能方法的未来方向及其在精准医学中的应用.
主要方法:
- 对临床数据分析的传统和新兴ML技术的审查.
- 讨论大型语言模型 (LLM) 和基础模型.
- 探索数学建模,可解释的人工智能和多式学习.
主要成果:
- 目前的ML方法有效分析各种临床数据,包括过敏疾病数据集.
- 像LLM和数字双胞胎这样的新兴人工智能方法对个性化医疗非常有前途.
- 整合各种数据类型 (多模式学习) 是未来的一个关键方向.
结论:
- 人工智能,尤其是机器学习,为推进临床研究和医疗保健提供了强有力的工具,
- 未来的方向包括可解释的人工智能,数字双胞胎,以及用于增强精确医学的多模式学习.
- 医生参与和道德考虑 (公平,隐私,自主) 对于负责任的人工智能实施至关重要.
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