[医疗人工智能发展的现状,应用场景,挑战和建议:国内和国际视角]
1Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing 100191, China.
概括
由深度学习驱动的医疗人工智能 (AI) 正在迅速发展. 这篇评论考察了AI.
科学领域:
- 医学的人工智能 (AI)
- 医疗保健中的深度学习
- 医学中的大型语言模型 (LLM)
背景情况:
- 医疗人工智能的技术框架正在从象征性推理演变为深度学习和法学士.
- 来自中国和美国等国家的政策支持正在推动医学领域的人工智能发展.
- 对国内和国际大规模人工智能模型的分析侧重于数据,计算能力和算法.
研究的目的:
- 审查人工智能技术在医疗领域的进展.
- 分析大规模人工智能模型的当前开发状态.
- 概述AI在关键医疗应用场景中的进展,并确定挑战.
主要方法:
- 审查人工智能技术的发展和政策支持.
- 对大规模AI模型 (数据,计算能力,算法) 的分析.
- 在五个医疗应用场景中对人工智能进步的系统概述.
- 检查关键挑战和拟议的战略.
主要成果:
- 人工智能在精确预防,诊断,治疗,药物开发,传染病控制和医学教育方面正在迅速发展.
- 关键的挑战包括数据质量,模型解释性,因果关系,幻觉控制,公平性,过拟合和安全验证.
- 建议制定战略,以应对医疗人工智能开发中的这些关键挑战.
结论:
- 医疗人工智能,特别是深度学习和LLM,在各种应用中取得了重大进展.
- 解决数据,可解释性和安全方面的挑战对于未来的发展至关重要.
- 为推进医疗人工智能提供了全面的战略和建议.
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