在瘤学中基于人工智能的癌症模型:从诊断到ADC药物预测
Navid Sobhani1, Fernanda G Kugeratski2, Sergio Venturini3
1Department of Leukemia and Cancer Biology, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
Cancers
|November 13, 2025
概括
人工智能 (AI) 为瘤学挑战提供解决方案,例如数据稀缺性和透明度. 可解释的人工智能和联合学习可以提高诊断精度和药物发现,以获得更好的癌症治疗结果.
科学领域:
- 在瘤学瘤学.
- 医疗信息学 医疗信息学
- 人工智能的人工智能
背景情况:
- 人工智能 (AI) 正在越来越多地影响瘤学实践.
- 关键的挑战包括数据限制,隐私问题以及模型透明度的缺乏.
- 这些问题阻碍了AI在癌症治疗中的发展和采用.
研究的目的:
- 审查用于改善癌症诊断和药物发现的新型AI模型.
- 探索可解释的人工智能和联合学习在克服当前局限性的潜力.
- 讨论人工智能在个性化癌症治疗和预测治疗疗效方面的作用,特别是在抗体-药物联合体 (ADC) 中.
主要方法:
- 审查人工智能的最新进展,重点关注可解释的人工智能和联合学习.
- 分析AI在医学诊断,药物发现和治疗选择中的应用.
- 探索AI对治疗结果的预测能力,包括ADC疗效.
主要成果:
- 可解释的人工智能可以建立信任,并促进临床医生和患者之间采用人工智能.
- 联合学习使跨机构的协作模式培训能够在没有数据共享的情况下实现.
- 人工智能模型显示出更精确,更快速的诊断和个性化癌症治疗选择的前景.
结论:
- 新型人工智能方法,包括可解释的人工智能和联合学习,对于推进瘤学至关重要.
- 这些技术可以解决数据限制和透明度问题,促进更广泛的AI实施.
- 人工智能有可能通过提高诊断准确度,加速药物发现和优化治疗策略 (包括ADC疗法) 来彻底改变癌症护理.
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