临床诊断和治疗中的医学多式联络基础模型:应用,挑战和未来方向
Kai Sun1, Siyan Xue1, Fuchun Sun2
1School of Biomedical Engineering, Tsinghua University, Beijing, China.
Artificial intelligence in medicine
|September 19, 2025
概括
深度学习的进步正在通过医学多式模式基础模型 (MMFMs) 彻底改变临床诊断和治疗. 这些模型通过整合各种数据集来提高患者的治疗结果和高效的医疗保健工作流程来增强精确医学.
科学领域:
- 人工智能的人工智能
- 医疗信息学 医疗信息学
- 深度学习 (Deep Learning) 是一种深度学习.
背景情况:
- 深度学习显著推进了临床诊断和治疗.
- 多器官和多模式数据集的兴起推动了医学多模式基础模型 (MMFMs) 的发展.
- 对于临床任务,MMFMs提供了强大的概括性和代表性能力.
研究的目的:
- 为医疗多式模式基础模型 (MMFMs) 的最新发展提供全面的分析.
- 专注于数据集,模型架构和MMFMs的临床应用.
- 探索优化多式联络表现的挑战和机遇.
主要方法:
- 对临床应用的深度学习近期进展的回顾.
- 对大型医疗多式模式基础模型 (MMFMs) 的分析.
- 检查数据集,模型架构和临床用例.
主要成果:
- MMFMs越来越适合于各种临床任务,从早期诊断到个性化治疗.
- 这些模型显示了提高诊断精度和治疗疗效的潜力.
- 进步正在塑造医疗保健的未来,以改善患者的治疗结果.
结论:
- MMFMs代表了朝着精准医学迈出的重要一步.
- 优化多式联运表示是释放MMFM的全部潜力的关键.
- 这些模型承诺更高效的临床工作流程和增强的患者护理.
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