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人工智能驱动的个性化医学:改变炎症性肠病的临床实践
Marietta Iacucci1, Giovanni Santacroce1, Maeda Yasuharu1
1APC Microbiome Ireland, College of Medicine and Health, University College Cork, Cork, Ireland.
人工智能 (AI) 通过整合内镜,组织学和多omics数据来提高炎症性肠病 (IBD) 护理,以准确诊断和预测结果. 这种人工智能驱动的"内因组学"方法可以为IBD患者提供个性化医疗.
科学领域:
- 胃肠道学和计算生物学
背景情况:
- 炎症性肠病 (IBD) 具有显著的临床异质性,使诊断和个性化治疗复杂化.
- 目前的诊断方法如内镜和组织学在预测长期结果方面存在局限性,导致患者管理不足最佳.
研究的目的:
- 探索人工智能 (AI) 在改善IBD结果的评估和预测方面的变革潜力.
- 为实时,个性化的IBD管理引入人工智能支持的集成"内因组学"方法的概念.
主要方法:
- 利用人工智能进行标准化和准确的疾病评估,包括肠道屏障愈合.
- 自动化整合多omics数据,以增强患者概况.
- 融合内镜,组织学和分子数据,以实现全面的方法.
主要成果:
- 人工智能为深度治疗提供了新的见解,并有助于发现新的治疗点.
- 自动化的多omics集成改善了患者分层和治疗策略的个性化.
- 由人工智能驱动的"内因组学"方法提供了精细的风险分层和治疗精度.
结论:
- 未来的IBD护理涉及一个AI-enabled"内因组学"的整合方法.
- 这种范式的转变,尽管采用挑战,可以显著推进精确医学在常规的临床实践.
- 人工智能整合有望提高IBD患者的治疗精度和个性化干预措施.
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