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

TL;DR

This paper introduces XpertXAI, a human-centric, expert-guided model for interpretable lung cancer detection from chest X-rays, outperforming existing explainability methods in clinical relevance and accuracy.

Contribution

The work extends prior concept bottleneck models to a scalable, multi-pathology framework that provides clinically meaningful explanations aligned with radiologist reasoning.

Findings

XpertXAI outperforms baseline methods in predictive accuracy.

It provides concept-level explanations consistent with expert annotations.

Existing explainability techniques often fail to produce meaningful clinical explanations.

Abstract

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.