Explainability Through Human-Centric Design for XAI in Lung Cancer Detection

Deep learning models have shown promise in lung pathology detection from chest X-rays, but widespread clinical adoption remains limited due to opaque model decision-making. In prior work, we introduced ClinicXAI, a human-centric, expert-guided concept bottleneck model (CBM) designed for interpretable lung cancer diagnosis. We now extend that approach and present XpertXAI, a generalizable expert-driven model that preserves human-interpretable clinical concepts while scaling to detect multiple lung pathologies. Using a high-performing InceptionV3-based classifier and a public dataset of chest X-rays with radiology reports, we compare XpertXAI against leading post-hoc explainability methods and an unsupervised CBM, XCBs. We assess explanations through comparison with expert radiologist annotations and medical ground truth. Although XpertXAI is trained for multiple pathologies, our expert validation focuses on lung cancer. We find that existing techniques frequently fail to produce clinically meaningful explanations, omitting key diagnostic features and disagreeing with radiologist judgments. XpertXAI not only outperforms these baselines in predictive accuracy but also delivers concept-level explanations that better align with expert reasoning. While our focus remains on explainability in lung cancer detection, this work illustrates how human-centric model design can be effectively extended to broader diagnostic contexts - offering a scalable path toward clinically meaningful explainable AI in medical diagnostics.

CoRPA: Adversarial Image Generation for Chest X-rays Using Concept Vector Perturbations and Generative Models

Deep learning models for medical image classification tasks are becoming widely implemented in AI-assisted diagnostic tools, aiming to enhance diagnostic accuracy, reduce clinician workloads, and improve patient outcomes. However, their vulnerability to adversarial attacks poses significant risks to patient safety. Current attack methodologies use general techniques such as model querying or pixel value perturbations to generate adversarial examples designed to fool a model. These approaches may not adequately address the unique characteristics of clinical errors stemming from missed or incorrectly identified clinical features. We propose the Concept-based Report Perturbation Attack (CoRPA), a clinically-focused black-box adversarial attack framework tailored to the medical imaging domain. CoRPA leverages clinical concepts to generate adversarial radiological reports and images that closely mirror realistic clinical misdiagnosis scenarios. We demonstrate the utility of CoRPA using the MIMIC-CXR-JPG dataset of chest X-rays and radiological reports. Our evaluation reveals that deep learning models exhibiting strong resilience to conventional adversarial attacks are significantly less robust when subjected to CoRPA's clinically-focused perturbations. This underscores the importance of addressing domain-specific vulnerabilities in medical AI systems. By introducing a specialized adversarial attack framework, this study provides a foundation for developing robust, real-world-ready AI models in healthcare, ensuring their safe and reliable deployment in high-stakes clinical environments.

Transparent and Clinically Interpretable AI for Lung Cancer Detection in Chest X-Rays

The rapidly advancing field of Explainable Artificial Intelligence (XAI) aims to tackle the issue of trust regarding the use of complex black-box deep learning models in real-world applications. Existing post-hoc XAI techniques have recently been shown to have poor performance on medical data, producing unreliable explanations which are infeasible for clinical use. To address this, we propose an ante-hoc approach based on concept bottleneck models which introduces for the first time clinical concepts into the classification pipeline, allowing the user valuable insight into the decision-making process. On a large public dataset of chest X-rays and associated medical reports, we focus on the binary classification task of lung cancer detection. Our approach yields improved classification performance in lung cancer detection when compared to baseline deep learning models (F1 > 0.9), while also generating clinically relevant and more reliable explanations than existing techniques. We evaluate our approach against post-hoc image XAI techniques LIME and SHAP, as well as CXR-LLaVA, a recent textual XAI tool which operates in the context of question answering on chest X-rays.