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期刊来源：Nat Med

原文链接：https://doi.org/10.1038/s41591-024-02887-x

摘要内容如下：

构建可信且透明的基于图像的医疗人工智能（AI）系统需要能够在开发管道的所有阶段（从训练模型到部署后监控）询问数据和模型。理想情况下，数据和相关的人工智能系统可以使用医生熟悉的术语来描述，但这需要使用语义上有意义的概念对医学数据集进行密集注释。在本研究中，我们提出了一种名为Monet（Medical Concept Retriever）的基础模型方法，该方法学习如何将医学图像与文本联系起来，并对概念存在的图像进行密集评分，以支持医学AI开发和部署中的重要任务，如数据审核、模型审核和模型解释。由于疾病、肤色和成像模式的异质性，皮肤病学为Monet的多功能性提供了一个苛刻的使用案例。我们基于105，550张皮肤病学图像和大量医学文献中的自然语言描述对莫奈进行了训练。经委员会认证的皮肤科医生验证，Monet可以准确地注释皮肤病学图像中的概念，与基于临床图像的先前概念注释的皮肤病学数据集建立的监督模型竞争。我们演示了Monet如何在整个AI系统开发管道中实现AI透明度，从构建固有的可解释模型到数据集和模型审计，包括剖析AI临床试验结果的案例研究。

英文原文如下：

Abstracts

Building trustworthy and transparent image-based medical artificial intelligence (AI) systems requires the ability to interrogate data and models at all stages of the development pipeline, from training models to post-deployment monitoring. Ideally, the data and associated AI systems could be described using terms already familiar to physicians, but this requires medical datasets densely annotated with semantically meaningful concepts. In the present study, we present a foundation model approach, named MONET (medical concept retriever), which learns how to connect medical images with text and densely scores images on concept presence to enable important tasks in medical AI development and deployment such as data auditing, model auditing and model interpretation. Dermatology provides a demanding use case for the versatility of MONET, due to the heterogeneity in diseases, skin tones and imaging modalities. We trained MONET based on 105,550 dermatological images paired with natural language descriptions from a large collection of medical literature. MONET can accurately annotate concepts across dermatology images as verified by board-certified dermatologists, competitively with supervised models built on previously concept-annotated dermatology datasets of clinical images. We demonstrate how MONET enables AI transparency across the entire AI system development pipeline, from building inherently interpretable models to dataset and model auditing, including a case study dissecting the results of an AI clinical trial.

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