快速审查:在医疗保健中,多模式大语言模型的使用越来越多
Pallavi Gupta1, Zhihong Zhang2, Meijia Song3
1Columbia University, School of Nursing, NY, United States.
Journal of biomedical informatics
|August 3, 2025
概括
多模式大语言模型 (MLLMs) 通过整合各种数据,显示出医疗保健的巨大潜力. 然而,不一致的评估和有限的临床部署强调了需要标准化和以人为中心的设计,以确保安全采用.
科学领域:
- 人工智能在医学中的应用
- 多模式大型语言模型 (MLLMs)
- 医疗保健技术 技术 医疗保健 技术
背景情况:
- 大型语言模型 (LLM) 最近的进步导致了多模式LLM (MLLM) 的开发,这些LLM能够处理文本以外的各种数据类型.
- 虽然MLLM显示出显著的前景,但经验证据显示其在医疗保健领域的影响和应用存在显著的差距.
- 本综述旨在巩固当前关于MLLM在医疗保健中的应用知识,强调它们的变革潜力.
研究的目的:
- 总结多式大型语言模型 (MLLMs) 在医疗保健中的新兴应用.
- 通过综合数据分析,突出MLLMs在彻底改变卫生实践方面的潜力.
- 在当前的研究环境中识别MLLM在临床环境中采用的差距和挑战.
主要方法:
- 根据世界卫生组织 (WHO) 的快速审查方法和PRISMA标准,在2024年8月进行了快速文献审查.
- 在主要数据库 (Scopus,Medline,PubMed,ACM数字图书馆) 和领先的AI/ML和医学成像会议上进行了搜索.
- 应用了包含和排除标准来选择有关MLLM医疗保健应用的相关文章进行分析.
主要成果:
- 审查确定了115篇文章,其中39篇被纳入最终分析; 77%在2024年发表,表明该领域的快速出现.
- 在地理上,80%的研究来自亚洲和北美,重点是诊断应用 (放射学,病理学,眼科) 在81%的病例中.
- 虽然提示策略很常见 (80%),但评估指标不一致 (报告的准确率为67%),错误分析有限 (18%),可解释性验证 (13%) 和临床部署 (3%).
结论:
- 多式联网大型语言模型 (MLLMs) 具有通过综合多式联网数据分析改变医疗保健的巨大潜力.
- 仍然存在重大挑战,包括方法不一致,验证不足和部署战略不发达.
- 标准化的评估指标,结构化的错误分析和以人为中心的设计对于安全,可扩展和值得信赖的MLLMs临床采用至关重要.
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