The main challenges hindering the adoption of deep learning-based systems in clinical settings are the scarcity of annotated data and the lack of interpretability and trust in these systems. Concept Bottleneck Models (CBMs) offer inherent interpretability by constraining the final disease prediction on a set of human-understandable concepts. However, this inherent interpretability comes at the cost of greater annotation burden. Additionally, adding new concepts requires retraining the entire system. In this work, we introduce a novel two-step methodology that addresses both of these challenges. By simulating the two stages of a CBM, we utilize a pretrained Vision Language Model (VLM) to automatically predict clinical concepts, and a Large Language Model (LLM) to generate disease diagnoses based on the predicted concepts. We validate our approach on three skin lesion datasets, demonstrating that it outperforms traditional CBMs and state-of-the-art explainable methods, all without requiring any training and utilizing only a few annotated examples. The code is available at https://github.com/CristianoPatricio/2-step-concept-based-skin-diagnosis.

翻译：阻碍深度学习系统在临床环境中应用的主要挑战在于标注数据稀缺以及这些系统缺乏可解释性与可信度。概念瓶颈模型通过将最终疾病预测约束在一组人类可理解的概念上，提供了固有的可解释性。然而，这种固有可解释性是以更高的标注负担为代价的。此外，添加新概念需要重新训练整个系统。本研究提出了一种新颖的两步法，旨在同时解决这两大挑战。通过模拟概念瓶颈模型的两个阶段，我们利用预训练的视觉语言模型自动预测临床概念，并采用大型语言模型基于预测概念生成疾病诊断。我们在三个皮肤病变数据集上验证了该方法，证明其性能优于传统概念瓶颈模型和最先进的可解释方法，且无需任何训练，仅需少量标注样本。代码发布于 https://github.com/CristianoPatricio/2-step-concept-based-skin-diagnosis。