精准医学中的多模式人工智能:整合基因组学,成像和EHR数据以获得临床见解
Shahper Nazeer Khan1,2, Danishuddin3, Mohd Wajid Ali Khan4,5
1Integral Centre of Excellence for Interdisciplinary Research (ICEIR), Integral University, Lucknow, India.
Frontiers in artificial intelligence
|January 23, 2026
概括
人工智能 (AI) 将多模式数据集成到个性化医疗中,改善疾病的检测和治疗. 这种方法增强了诊断和药物开发,重新定义了个性化的医疗保健服务.
科学领域:
- 生物医学信息学 生物医学信息学
- 计算生物学 计算生物学
- 数字健康数字健康
背景情况:
- 精准医疗的目标是个性化治疗策略.
- 人工智能 (AI) 是整合多模式患者数据的核心.
- 不同质的数据流包括基因组,成像和电子健康记录 (EHR).
研究的目的:
- 审查多模式人工智能在精密医学中的变革潜力.
- 强调AI在多omics数据集成,预测建模和临床决策支持中的作用.
- 批判性地评估人工智能驱动的精确医疗保健中的挑战.
主要方法:
- 人工智能支持的多模式数据集成巩固了多样化的数据流.
- 机器学习 (ML) 和深度学习 (DL) 算法提取复杂的关联.
- 审查数字健康中的AI应用,包括生物传感器和实时监控.
主要成果:
- 人工智能增强了早期疾病检测,生物标志物发现和药物开发.
- 改善了诊断精度,风险分层和针对患者的具体干预措施.
- 人工智能通过数字健康工具促进治疗计划的持续改进.
结论:
- 人工智能和多模式数据的融合正在重新定义个性化的医疗保健.
- 人工智能驱动的精准医学对瘤学,神经学和心血管医学有重大影响.
- 解决数据互操作性,偏见和隐私等挑战对于人工智能采用至关重要.
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