# Integrating Clinical Knowledge into Concept Bottleneck Models

arXiv: https://arxiv.org/abs/2407.06600 · HTML: https://mlsanity.com/paper/2407.06600/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 24 references that are in the map.
- First posted on arXiv 9 Jul 2024.

## Builds on (most cited first, 12 of 12)

- [Concept Bottleneck Models](https://mlsanity.com/paper/2007.04612/) (2020)
- [Post-hoc Concept Bottleneck Models](https://mlsanity.com/paper/2205.15480/) (2022)
- [Label-Free Concept Bottleneck Models](https://mlsanity.com/paper/2304.06129/) (2023)
- [Evaluating Deep Neural Networks Trained on Clinical Images in Dermatology with the Fitzpatrick 17k Dataset](https://mlsanity.com/paper/2104.09957/) (2021)
- [Disparities in Dermatology AI: Assessments Using Diverse Clinical Images](https://mlsanity.com/paper/2111.08006/) (2021)
- [SkinCon: A skin disease dataset densely annotated by domain experts for fine-grained model debugging and analysis](https://mlsanity.com/paper/2302.00785/) (2023)
- [Towards Trustable Skin Cancer Diagnosis via Rewriting Model's Decision](https://mlsanity.com/paper/2303.00885/) (2023)
- [WBCAtt: A White Blood Cell Dataset Annotated with Detailed Morphological Attributes](https://mlsanity.com/paper/2306.13531/) (2023)
- [Interpretable and intervenable ultrasonography-based machine learning models for pediatric appendicitis](https://mlsanity.com/paper/2302.14460/) (2023)
- [Multi-Attribute Attention Network for Interpretable Diagnosis of Thyroid Nodules in Ultrasound Images](https://mlsanity.com/paper/2207.04219/) (2022)
- [A Novel Multi-Task Model Imitating Dermatologists for Accurate Differential Diagnosis of Skin Diseases in Clinical Images](https://mlsanity.com/paper/2307.08308/) (2023)
- [Benchmarking White Blood Cell Classification Under Domain Shift](https://mlsanity.com/paper/2303.01777/) (2023)

## Built on by (most cited first, 17 of 17)

- [Self-eXplainable AI for Medical Image Analysis: A Survey and New Outlooks](https://mlsanity.com/paper/2410.02331/) (2024)
- [Human-AI Co-design for Clinical Prediction Models](https://mlsanity.com/paper/2601.09072/) (2026)
- [Learning Concept-Driven Logical Rules for Interpretable and Generalizable Medical Image Classification](https://mlsanity.com/paper/2505.14049/) (2025)
- [WISE: Weak-Supervision-Guided Step-by-Step Explanations for Multimodal LLMs in Image Classification](https://mlsanity.com/paper/2509.17740/) (2025)
- [Explainability of Large Language Models: Opportunities and Challenges toward Generating Trustworthy Explanations](https://mlsanity.com/paper/2510.17256/) (2025)
- [Tree of Concepts: Interpretable Continual Learners in Non-Stationary Clinical Domains](https://mlsanity.com/paper/2604.17089/) (2026)
- [Training-free Test-time Improvement for Explainable Medical Image Classification](https://mlsanity.com/paper/2506.18070/) (2025)
- [Explainable Artificial Intelligence in Biomedical Image Analysis: A Comprehensive Survey](https://mlsanity.com/paper/2507.07148/) (2025)
- [Automated Genomic Interpretation via Concept Bottleneck Models for Medical Robotics](https://mlsanity.com/paper/2510.01618/) (2025)
- [Learning Label-Efficient Interpretable Medical Image Diagnosis via Semi-supervised Hypergraph Concept Bottleneck Model](https://mlsanity.com/paper/2606.01698/) (2026)
- [A Tool Bottleneck Framework for Clinically-Informed and Interpretable Medical Image Understanding](https://mlsanity.com/paper/2512.21414/) (2025)
- [LLM Augmented Intervenable Multimodal Adaptor for Post-operative Complication Prediction in Lung Cancer Surgery](https://mlsanity.com/paper/2601.14154/) (2026)
- [Vision-Language Models Encode Clinical Guidelines for Concept-Based Medical Reasoning](https://mlsanity.com/paper/2603.08921/) (2026)
- [DCG-Net: Dual Cross-Attention with Concept-Value Graph Reasoning for Interpretable Medical Diagnosis](https://mlsanity.com/paper/2603.20325/) (2026)
- [Measuring What Matters: Synthetic Benchmarks for Concept Bottleneck Models](https://mlsanity.com/paper/2606.04326/) (2026)
- [NeRD: Neuro-Symbolic Rule Distillation for Efficient Ontology-Grounded Chain-of-Thought in Medical Image Diagnosis](https://mlsanity.com/paper/2606.15617/) (2026)
- [Agent-Guided Relational Concept Discovery: Toward Interpretable Surgical Margin Assessment](https://mlsanity.com/paper/2607.21437/) (2026)

## Abstract (arXiv)

Concept bottleneck models (CBMs), which predict human-interpretable concepts (e.g., nucleus shapes in cell images) before predicting the final output (e.g., cell type), provide insights into the decision-making processes of the model. However, training CBMs solely in a data-driven manner can introduce undesirable biases, which may compromise prediction performance, especially when the trained models are evaluated on out-of-domain images (e.g., those acquired using different devices). To mitigate this challenge, we propose integrating clinical knowledge to refine CBMs, better aligning them with clinicians' decision-making processes. Specifically, we guide the model to prioritize the concepts that clinicians also prioritize. We validate our approach on two datasets of medical images: white blood cell and skin images. Empirical validation demonstrates that incorporating medical guidance enhances the model's classification performance on unseen datasets with varying preparation methods, thereby increasing its real-world applicability.