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基于人工智能的生物标志物用于瘤学治疗决策

Marta Ligero¹, Omar S M El Nahhas¹, Mihaela Aldea²

¹Else Kroener Fresenius Center for Digital Health, Medical Faculty Carl Gustav Carus, Dresden University of Technology (TUD), Dresden, Germany.

Trends in cancer

|January 15, 2025

概括

人工智能 (AI) 提供了具有成本效益的生物标志物,以帮助癌症治疗决策,解决新疗法和个性化医疗成本的复杂性. 这篇评论探讨了AI解决方案,如深度学习和临床实践的大型语言模型.

科学领域:

在瘤学瘤学.
医疗信息学医疗信息学
生物技术是生物技术.

背景情况:

由于新型免疫检查点抑制剂 (ICI) 和向疗法,癌症治疗环境越来越复杂.
美国食品和药物管理局批准的快速步伐表明,在5年内,治疗选择将增加10倍.
个性化医疗的高成本加剧了癌症护理的社会经济差异.

研究的目的:

审查基于人工智能 (AI) 的解决方案,以支持癌症治疗决策.
探索深度学习 (DL) 用于医学成像和大语言模型 (LLM) 用于电子健康记录 (EHR) 的使用.
提出将成本效益高的AI生物标志物整合到常规临床实践中的途径.

主要方法:

关于人工智能在瘤学中的应用现有文献的综述.
深度学习 (DL) 分析用于医疗图像分析.
对处理电子健康记录 (EHR) 的大型语言模型 (LLM) 的评估.

主要成果:

包括DL和LLM在内的AI可以提供具有成本效益的生物标志物,以支持复杂的癌症治疗决策.
这些技术有潜力通过提高对个性化医学见解的可访问性来缓解社会经济差异.
确定了人工智能在常规临床实践中采用的当前局限性.

结论:

由人工智能驱动的工具提供了有希望的解决方案,以应对现代癌症治疗的复杂性.
从人工智能获得的具有成本效益的生物标志物可以增强临床决策,并可能减少医疗保健差异.
为了在常规瘤治疗中广泛采用人工智能,需要进一步开发和验证.

关键词:

人工智能的人工智能是人工智能.生物标志物生物标志物医学成像医学成像瘤学在瘤学方面.个性化医疗是个性化的医疗.

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