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arxiv: 2505.09755 · v3 · submitted 2025-05-14 · 💻 cs.AI

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

Amy Rafferty , Rishi Ramaesh , Ajitha Rajan This is my paper

Pith reviewed 2026-05-22 15:09 UTC · model grok-4.3

classification 💻 cs.AI
keywords explainable AIconcept bottleneck modelslung cancer detectionchest X-rayshuman-centric designmedical imagingradiologist alignmentmulti-pathology detection
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The pith

XpertXAI embeds expert clinical concepts into a bottleneck model to achieve higher accuracy and explanations that align with radiologist reasoning for lung cancer detection.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper presents XpertXAI as a scalable extension of an earlier concept bottleneck approach for chest X-ray analysis. It seeks to demonstrate that inserting human-selected clinical concepts as an intermediate layer allows the model to maintain strong predictive performance across multiple lung pathologies while generating explanations that match expert judgments more closely than standard post-hoc methods. This matters for clinical use because opaque deep learning outputs have slowed adoption even when models detect pathology accurately. The work tests the model on public X-ray datasets with radiology reports, validates explanations against radiologist annotations for lung cancer, and contrasts results against leading baselines and an unsupervised concept model. If the approach holds, it points to a practical route for building medical AI that doctors can inspect and trust without sacrificing diagnostic power.

Core claim

XpertXAI is a generalizable expert-driven concept bottleneck model built on an InceptionV3 classifier that preserves human-interpretable clinical concepts while scaling from single-disease to multi-pathology detection on chest X-rays; when evaluated against post-hoc explainability techniques and the unsupervised XCBs baseline, it delivers superior predictive accuracy and concept-level explanations that align more closely with expert radiologist annotations and medical ground truth for lung cancer.

What carries the argument

The expert-guided concept bottleneck layer that converts raw image features into a small set of human-chosen clinical concepts before producing the final diagnosis, thereby enforcing interpretability while retaining accuracy.

If this is right

  • Post-hoc explainability methods commonly omit key diagnostic features and conflict with radiologist judgments on chest X-rays.
  • Concept-level outputs remain clinically meaningful even after the model is trained for multiple lung pathologies at the same time.
  • Human-centric bottleneck design improves both accuracy and explanation quality compared with unsupervised concept models.
  • The same architecture supplies a template for extending interpretable AI beyond lung cancer to wider diagnostic tasks.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same expert-concept approach could transfer to other imaging modalities such as CT or MRI if suitable clinical concepts are identified.
  • Hospitals might reduce reliance on separate explanation modules by training models with built-in concept layers from the start.
  • If the chosen concepts prove robust, the method could shorten regulatory review for medical AI by making decision logic more transparent upfront.

Load-bearing premise

Expert-selected clinical concepts placed in the bottleneck layer accurately capture how radiologists reason about diagnoses and continue to work when the model expands from one lung condition to several at once.

What would settle it

A direct comparison in which radiologists review the same cases and select different diagnostic concepts than those hard-coded in the model, or a test showing that predictive accuracy falls when the model is forced to handle multiple pathologies without those concepts.

Figures

Figures reproduced from arXiv: 2505.09755 by Ajitha Rajan, Amy Rafferty, Rishi Ramaesh.

Figure 1
Figure 1. Figure 1: For LIME, SHAP and Grad-CAM, (a) shows mean pixel [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Analysis by an expert radiologist of explanations generated for a subset of 40 cancerous and 20 healthy chest X-rays. The expert [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Explanations generated by each XAI technique for a cancerous chest X-ray. (a) shows the ground truth hilar mass. (b) shows LIME [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Example of a cancerous radiology report from the MIMIC [PITH_FULL_IMAGE:figures/full_fig_p005_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: The pipeline for XpertXAI: We take a chest X-ray as input, which is fed into a trained concept prediction model, producing [PITH_FULL_IMAGE:figures/full_fig_p006_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Example correct XpertXAI explanation generated on a [PITH_FULL_IMAGE:figures/full_fig_p007_6.png] view at source ↗
read the original 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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

1 major / 0 minor

Summary. The manuscript introduces XpertXAI as an extension of the authors' prior ClinicXAI work: a human-centric concept bottleneck model that incorporates expert-selected clinical concepts to enable interpretable detection of multiple lung pathologies from chest X-rays. It claims that XpertXAI outperforms post-hoc explainability baselines and an unsupervised CBM (XCBs) in predictive accuracy while producing concept-level explanations that better align with radiologist annotations and medical ground truth, although the expert validation is restricted to lung cancer despite multi-pathology training.

Significance. If the quantitative results hold, the work demonstrates a viable path for scaling expert-guided concept bottleneck models to multi-disease settings in medical imaging, which could improve clinical trust and adoption of deep learning for chest X-ray analysis by preserving human-interpretable concepts.

major comments (1)
  1. [Abstract] Abstract: The central claim that XpertXAI provides a scalable, generalizable approach for multiple lung pathologies is load-bearing yet unsupported, because the manuscript states that expert validation and comparison against radiologist annotations focus solely on lung cancer. This leaves the behavior of the expert-selected concepts in the bottleneck layer untested under multi-label conditions where concept co-occurrence and diagnostic interactions differ from the single-disease case.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for their constructive feedback and for identifying an important point about the scope of our claims. We address the major comment below and have revised the manuscript to ensure the claims accurately reflect the presented evidence.

read point-by-point responses

  1. Referee: The central claim that XpertXAI provides a scalable, generalizable approach for multiple lung pathologies is load-bearing yet unsupported, because the manuscript states that expert validation and comparison against radiologist annotations focus solely on lung cancer. This leaves the behavior of the expert-selected concepts in the bottleneck layer untested under multi-label conditions where concept co-occurrence and diagnostic interactions differ from the single-disease case.

    Authors: We thank the referee for this observation. The manuscript already states that expert validation is restricted to lung cancer, and we do not claim to have conducted radiologist annotation comparisons across all pathologies. XpertXAI is trained in a multi-label setting on a dataset containing multiple lung pathologies, with expert-selected concepts chosen for their relevance to the broader diagnostic task; predictive performance improvements are reported on this multi-pathology objective. We agree that direct testing of concept alignment and interactions under full multi-label conditions for additional diseases would provide stronger support for generalizability. To address this, we have revised the abstract, introduction, and discussion sections to qualify the generalizability statement as an illustration of the approach in a multi-pathology training regime, with detailed expert alignment demonstrated for lung cancer, while explicitly noting the limitation and outlining future multi-disease validation plans. These changes make the claims more precise without overstating the current results. revision: yes

Circularity Check

0 steps flagged

No significant circularity detected

full rationale

The paper extends prior ClinicXAI work by the same authors to a multi-pathology setting but grounds its central claims in new empirical results: training an InceptionV3-based CBM on a public chest X-ray dataset, comparing predictive accuracy against post-hoc methods and an unsupervised CBM baseline, and assessing concept-level explanations via direct comparison to independent expert radiologist annotations and medical ground truth. These steps rely on external data and annotations rather than reducing by construction to the prior paper's definitions or fitted parameters. The explicit note that expert validation focuses on lung cancer is a scope limitation, not a definitional loop. No load-bearing step equates a reported prediction or alignment result to its own inputs via self-citation or ansatz.

Axiom & Free-Parameter Ledger

1 free parameters · 1 axioms · 0 invented entities

The central claim rests on the validity of expert-chosen clinical concepts and the assumption that radiologist annotations constitute reliable ground truth for explanation quality. No new physical entities are postulated.

free parameters (1)
  • Expert-selected clinical concepts
    The bottleneck layer uses a set of human-interpretable concepts chosen by domain experts rather than discovered purely from data.
axioms (1)
  • domain assumption Expert radiologist annotations provide a reliable proxy for clinical ground truth when evaluating explanation quality.
    Validation of concept-level explanations is performed by direct comparison with radiologist judgments.

pith-pipeline@v0.9.0 · 5770 in / 1267 out tokens · 95485 ms · 2026-05-22T15:09:18.030390+00:00 · methodology

discussion (0)

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