医学中生成人工智能的当前审查:核心概念,应用和当前限制
Pouria Rouzrokh1,2,3,4,5, Bardia Khosravi6,7,8, Shahriar Faghani6,7
1Mayo Clinic AI Laboratory, Mayo Clinic, Rochester, MN, USA. rouzrokh.pouria@mayo.edu.
Current reviews in musculoskeletal medicine
|April 30, 2025
概括
生成型人工智能为医疗保健提供了新的工具,创建合成数据并协助记录和诊断等任务. 了解它的能力和局限性是医疗专业人员在不断发展的领域的关键.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
背景情况:
- 生成型人工智能 (AI) 模型创建新的合成数据,与歧视型模型不同.
- 关键家族包括扩散模型,大型语言模型 (LLM) 和大型多式模式模型 (LMM).
研究的目的:
- 为非工程医疗保健专业人员提供生成人工智能的基础概述.
- 澄清医学的生成人工智能的当前能力,应用和局限性.
主要方法:
- 对生成AI模型家族 (扩散,LLM,LMM) 的审查.
- 对当前和专业医疗保健应用程序的分析.
- 讨论包括知识差距,幻觉,偏见和可解释性在内的挑战.
主要成果:
- 生成型人工智能应用包括合成图像生成,自动化文档和训练模拟.
- 专业用途包括诊断辅助器,提取增强生成 (RAG) 和多代理工作流.
- 潜在的好处包括成像,文档,教育和决策支持.
结论:
- 生成型人工智能在医学中具有变革性的潜力,但面临着数据偏差和"幻觉"等挑战.
- 对医疗保健专业人员来说,了解这些技术至关重要,尽管AI不太可能取代医生.
- 导航监管和道德问题对于成功的整合至关重要.
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