Responsible Quantum Machine Learning for Medical Imaging: A Fairness Audit Across Sensitive Subgroups

Giovanni dos Santos Franco; Felipe Mahlow; Ellison Fernando Cardoso; José Adenaldo Santos Bittencourt; Felipe Fanchini

cs.LGOct 27, 2026

Responsible Quantum Machine Learning for Medical Imaging: A Fairness Audit Across Sensitive Subgroups

Authors: Giovanni dos Santos Franco, Felipe Mahlow, Ellison Fernando Cardoso, José Adenaldo Santos Bittencourt, Felipe Fanchini

Organizations: Faculty of Sciences, S˜ao Paulo State University (Unesp), Bauru, Brazil. · Laboratory of Magnetic Resonance in Neuroradiology (LIM-44), University of S˜ao Paulo Faculty of Medicine Clinics Hospital, S˜ao Paulo, Brazil. · Hospital Israelita Albert Einstein, S˜ao Paulo, Brazil.

Abstract

Quantum machine learning (QML) is increasingly explored for medical classification, yet its behaviour under subgroup-aware fairness criteria remains understudied. We present an empirical fairness audit of classical and quantum classifiers for medical imaging, assuming QML is not inherently fair. Two scenarios are evaluated: dermatological lesions on DDI (sensitive attribute: Fitzpatrick skin type) and chest X-rays on CheXpert-derived data (sensitive attribute: age group). Using ResNet-18 embeddings, we train SVM, MLP, QSVM and QCNN under a baseline and three mitigation settings: differentiable Equalized Odds (EO) regularisation, Gradient Reversal Learning (GRL), and both combined. Foundation models (PanDerm, EVA-X) serve as external references. Quantum classifiers may achieve competitive aggregate performance while still exhibiting subgroup disparities. Since a smaller subgroup AUC gap may stem from degrading the best group rather than improving the worst, each mitigation is also tested against a pre-specified non-inferiority margin relative to its baseline. Under this criterion, several configurations with the smallest disparity, notably QCNN with GRL on DDI, are not clinically acceptable due to loss of global discrimination. On DDI, QSVM with GRL+EO performed best and approached PanDerm in the performance/fairness trade-off; on CheXpert, QCNN with GRL achieved the highest aggregate performance but increased disparity, and EVA-X remained stronger. Fairness effects are model- and dataset-dependent, so QML requires empirical subgroup-aware evaluation. All quantum models run in classical simulation; we claim no quantum advantage.

Figures & tables

Figure 1: Bias auditing pipeline for classical and quantum machine learning in medical imaging. The workflow starts from two medical imaging datasets: DDI for dermatology and CheXpert for chest radiography. Sensitive attributes are defined using Fitzpatrick skin type groups for DDI and age groups for CheXpert. Images are encoded using a ResNet-18 model pretrained on ImageNet, and the resulting embeddings are used as inputs to classical and quantum classifiers. The models are evaluated under baseline, EO, GRL, and GRL+EO settings, followed by subgroup-aware analysis using predictive and fairness metrics.

Figure 2: Class distribution across sensitive groups in the evaluated datasets. Panel (a) shows the percentage distribution of benign and malignant cases across Fitzpatrick skin type groups in DDI. Panel (b) shows the percentage distribution of no pleural effusion and pleural effusion cases across age groups in CheXpert. The figure highlights subgroup-specific differences in class prevalence, supporting the need for fairness-aware evaluation across sensitive groups.

Figure 3: Fairness mitigation strategies used in the proposed evaluation. Panel (a) shows Gradient Reversal Learning (GRL), in which a shared encoder produces a representation used by both the clinical classifier and a sensitive-attribute adversary. The gradient reversal layer gradually reverses the adversarial gradient according to a scheduled coefficient, encouraging the representation to preserve disease-relevant information while reducing information about the sensitive group. Panel (b) shows the differentiable Equalized Odds (EO) penalty used during training. The predicted probabilities are separated by true class and sensitive group, group-wise mean probabilities are computed within each class, and the variance of these group-wise means is added to the binary cross-entropy loss. This encourages the model to reduce group-dependent differences in prediction behaviour for both negative and positive cases.

Model	Mitigation	AUC	Balanced acc.	F1-score
EVA-X (foundation)	—	$0.925\pm 0.029$	$0.846\pm 0.032$	$0.782\pm 0.043$
SVM	Baseline	$0.783\pm 0.032$	$0.732\pm 0.031$	$0.611\pm 0.044$
SVM	Eq. Odds	$0.797\pm 0.030$	$0.735\pm 0.031$	$0.617\pm 0.045$
SVM	GRL	$0.783\pm 0.031$	$0.737\pm 0.030$	$0.618\pm 0.044$
SVM	GRL+EO	$0.789\pm 0.032$	$0.709\pm 0.031$	$0.582\pm 0.044$
MLP	Baseline	$0.805\pm 0.029$	$0.753\pm 0.031$	$0.640\pm 0.046$

Table 1: Global predictive performance on CheXpert across models and fairness mitigation strategies. Values are reported as point estimates with standard deviations across seeds when available. The foundation model is reported as a reference baseline and marked with “—” because no fairness mitigation strategy was applied. Bold values indicate the highest point estimate among non-foundation models for each metric. Bold marks the highest point estimate only; because the reported deviations are seed-to-seed variability rather than confidence intervals, and are not paired across models, differences between configurations with overlapping deviations should not be read as evidence of superiority Altman and Bland (1995) .

Model	Mitigation	AUC	Balanced acc.	F1-score
PanDerm (foundation)	—	$0.814\pm 0.051$	$0.766\pm 0.043$	$0.640\pm 0.064$
SVM	Baseline	$0.771\pm 0.049$	$0.724\pm 0.042$	$0.568\pm 0.061$
SVM	Eq. Odds	$0.761\pm 0.050$	$0.704\pm 0.047$	$0.549\pm 0.065$
SVM	GRL	$0.755\pm 0.048$	$0.687\pm 0.045$	$0.533\pm 0.068$
SVM	GRL+EO	$0.733\pm 0.054$	$0.654\pm 0.045$	$0.484\pm 0.078$
MLP	Baseline	$0.776\pm 0.047$	$0.693\pm 0.046$	$0.543\pm 0.072$

Table 2: Global predictive performance on DDI across models and fairness mitigation strategies. Values are reported as point estimates with standard deviations across seeds when available. The foundation model is reported as a reference baseline and marked with “—” because no fairness mitigation strategy was applied. Bold values indicate the highest point estimate among non-foundation models for each metric. As in Table 1 , overlapping deviations do not support claims of superiority between configurations.

Figure 4: Baseline ROC curves on DDI by Fitzpatrick skin type group. Each panel shows the overall ROC curve and subgroup-specific curves for FST I–II, FST III–IV, and FST V–VI. The figure illustrates that global predictive performance may conceal subgroup-level variation in model discrimination.

Figure 5: Baseline ROC curves on CheXpert by age group. Each panel shows the overall ROC curve and subgroup-specific curves for the 0–40, 41–60, and 60+ age groups. The downstream classifiers show weaker discrimination for the youngest subgroup, highlighting the importance of subgroup-aware evaluation.

Figure 6: Performance–fairness trade-off across models and mitigation strategies. Each point represents a model and mitigation setting. The horizontal axis reports mean AUC, while the vertical axis reports AUC gap across sensitive groups. Colour indicates the mitigation strategy, marker shape indicates model type, and bubble size represents balanced accuracy. Downward movement alone does not imply clinical benefit; see Section 3.6 for the joint criterion.

Figure 7: AUC gap summary across models and mitigation strategies. The heatmap reports the AUC gap between the best-performing and worst-performing sensitive groups. Lower values indicate smaller subgroup disparity. The two panels compare DDI and CheXpert under baseline and fairness mitigation settings. The heatmap reports disparity only; it should be read together with Table 3 , which reports the accompanying change in global discrimination.

Figure 8: Performance–fairness trade-off using demographic parity gap. Each point represents a model and mitigation setting. The horizontal axis reports mean AUC, while the vertical axis reports demographic parity gap across sensitive groups. Colour indicates the mitigation strategy, marker shape indicates model type, and bubble size represents balanced accuracy.

Figure 9: Demographic parity gap summary across models and mitigation strategies. The heatmap reports the demographic parity gap across sensitive groups. Lower values indicate smaller differences in positive prediction rates. The two panels compare DDI and CheXpert under baseline and fairness mitigation settings.

Model	Mitigation	$\bm{\delta_{\mathrm{AUC}}}$	$\bm{\delta_{\Delta}}$	Acceptable
DDI (sensitive attribute: Fitzpatrick skin type)
SVM	Eq. Odds	$-0.010$	$-0.058$	Yes
SVM	GRL	$-0.016$	$-0.076$	Yes
SVM	GRL+EO	$-0.038$	$+0.057$	No
MLP	Eq. Odds	$-0.007$	$-0.030$	Yes
MLP	GRL	$+0.005$	$-0.067$	Yes

Table 3: Joint assessment of disparity reduction and non-inferiority for each mitigated configuration, relative to the unmitigated baseline of the same model family.

\delta_{\mathrm{AUC}}

is the change in global AUC (Eq. 18 ) and

\delta_{\Delta}

is the change in subgroup AUC gap (Eq. 19 ); negative

\delta_{\Delta}

indicates reduced disparity. A configuration is marked “Yes” only when it reduces disparity (

\delta_{\Delta}<0

) and does not lose more than

\varepsilon=0.02

AUC points (

\delta_{\mathrm{AUC}}\geq-0.02

). Values are derived from Tables 1 and 2 and from Figure 7 . Bold marks the largest admissible disparity reduction in each dataset.

Fairness in Medical Image Classification

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Comparative Study of Quantum and Classical Machine Learning Models in Binary Classification

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Anand Kumar Mishra, Ramanuj Awasthi

School of Engineering & Technology (UIET), CSJM University, Kanpur, Uttar Pradesh, India

Quantum Machine Learning Quantum Kernel Methods Binary Classification Variational Quantum Circuits

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Apr 27, 2026

Quantum Kernel Advantage over Classical Collapse in Medical Foundation Model Embeddings

We provide evidence of quantum kernel advantage under noiseless simulation in binary insurance classification on MIMIC-CXR chest radiographs using quantum support vector machines (QSVM) with frozen embeddings from three medical foundation models (MedSigLIP-448, RAD-DINO, ViT-patch32). We propose a two-tier fair comparison framework in which both classifiers receive identical PCA-q features. At Tier 1 (untuned QSVM vs. untuned linear SVM, C = 1 both sides), QSVM wins minority-class F1 in all 18 tested configurations (17 at p < 0.001, 1 at p < 0.01). The classical linear kernel collapses to majority-class prediction on 90-100% of seeds at every qubit count, while QSVM maintains non-trivial recall. At q = 11 (MedSigLIP-448 plateau center), QSVM achieves mean F1 = 0.343 vs. classical F1 = 0.050 (F1 gain = +0.293, p < 0.001) without hyperparameter tuning. Under Tier 2 (untuned QSVM vs. C-tuned RBF SVM), QSVM wins all seven tested configurations (mean gain +0.068, max +0.112). Eigenspectrum analysis reveals quantum kernel effective rank reaches 69.80 at q = 11, far exceeding linear kernel rank, while classical collapse remains C-invariant. A full qubit sweep reveals architecture-dependent concentration onset across models. Code: https://github.com/sebasmos/qml-medimage

Sebastian Cajas Ordóñez, Felipe Ocampo Osorio, Dax Enshan Koh +10

1MIT Critical Data, Massachusetts Institute of Technology, Cambridge, MA, USA · 2Clinical Research Center, Artificial Intelligence Unit, Fundaci´on Valle del Lili, Cali, Valle del Cauca, Colombia · 3Quantum Innovation Centre (Q.InC), Agency for Science, Technology and Research (A*STAR), 2 Fusionopolis Way, Innovis #08-03, Singapore 138634, Republic of Singapore +15

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Sep 25, 2026

Quantum Diffusion Models for Medical Image Analysis

Quantum Machine Learning is a novel field of research aimed at devising machine learning approaches exploiting principles of quantum mechanics, such as superposition, entanglement and interference. In this context, we present a scalable hybrid Quantum Diffusion Model, and evaluate its use for medical image analysis. Specifically, our method is based on a Discrete-Time Quantum Walk algorithm, executed on a real quantum device, to model the forward dynamics of the diffusion model. For the backward step of the diffusion model, we devise and evaluate a classical learning model, which is used to reversely denoise the data. In contrast with other existing attempts at applying quantum machine learning for image analysis tasks, severely limited by the size of existing quantum devices, our method allows to process real-world large size medical data. In particular, we present results on grayscale and RGB images, as well as 3D volumes of moderate sizes. We benchmark our results by reproducing an alternative classical counterpart model, based on diffusion models on discrete state spaces. By doing so, we compare the generation capabilities of both models in terms of three distinct state-of-the-art metrics in the field of image generation, showing the competitive, promising results of our approach.

Francesco Aldo Venturelli, Stefano Martina, Marco Parigi +3

BCN Medtech, Department of Engineering, Universitat Pompeu Fabra, Barcelona, Spain · Barcelona Supercomputing Center, Barcelona, Spain · Department of Physics and Astronomy, University of Florence, Sesto Fiorentino, Florence, Italy +2

Quantum Generative Modeling Diffusion Models Medical Image Synthesis Hybrid Quantum-Classical ML

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Abstract

Fairness in Medical Image Classification

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