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一个医疗多式联络大型语言模型,用于未来的流行病

Fenglin Liu¹, Tingting Zhu², Xian Wu³

¹Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, UK. fenglin.liu@eng.ox.ac.uk.

NPJ digital medicine

|December 2, 2023

概括

一个新的医疗多式联络大型语言模型 (Med-MLLM) 从未标记的数据中有效地学习,使得对像COVID-19这样的罕见疾病能够快速适应,并且标签最小. 这种人工智能可以在各种数据类型和语言中改善临床决策.

科学领域:

人工智能在医学中的应用
医学成像分析医学成像分析
在医疗保健中的自然语言处理.

背景情况:

监督深度神经网络需要大量的标记数据,限制它们的应用到罕见疾病.
医生的工作量和诊断效率可以通过人工智能辅助的临床决策来提高.
未标记的医疗数据代表了人工智能模型培训的庞大,未得到充分利用的资源.

研究的目的:

开发一种能够从未标记的数据中学习的医疗多式联络大型语言模型 (Med-MLLM).
为了能够快速部署和适应AI模型用于罕见疾病的有限的标记数据.
创建一个多功能的人工智能工具,支持涉及视觉 (射线图) 和文字 (报告) 医疗数据的临床任务.

主要方法:

开发了一个Med-MLLM用于X光学表示学习,集成图像理解,文本语义和从未标记的数据中的临床表型.
利用多式联络数据,包括胸部X射线,CT扫描,医疗报告和临床笔记.
评估了模型在 COVID-19 数据集上的表现,在回顾和前性设置中,在不同的变体,语言和下游任务 (报告,诊断,预后) 中.

主要成果:

医学MLLM证明了从未标记的数据中有效地学习广泛的医学知识.
该模型表现出对罕见疾病的快速适应性,仅需要有限的标签才能部署.
在各种数据集,语言 (英语,中文,西班牙语) 和任务中实现了准确而强大的COVID-19决策支持,即使使用最小的标记数据.

结论:

Med-MLLM提供了一种强大的解决方案,可以利用未标记的医疗数据来克服罕见疾病诊断中的数据稀缺性.
该模型的多式联通能力和适应性增强了其对现实世界的临床决策支持系统的实用性.
这种方法对改善面对新兴和罕见疾病的诊断准确性和效率具有重大前景.

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