Interpretable AI in Med Diagnostics

• Interpretable AI in medical diagnostics is the development of transparent machine learning models that provide clear, auditable reasoning alongside diagnostic predictions.
• It employs techniques such as saliency maps, concept bottlenecks, surrogate models, and neuro-symbolic frameworks to balance accuracy with explainability.
• Practical applications in imaging, pathology, and multi-modal diagnostics demonstrate enhanced clinical trust, regulatory compliance, and improved decision-making.

Interpretable AI in medical diagnostics refers to the development and deployment of machine learning models whose predictions can be understood, scrutinized, and acted upon by clinicians and other healthcare professionals. Interpretable AI is designed to provide diagnostic outputs together with clear, auditable reasoning, addressing the vital need for transparency, trust, and regulatory compliance in high-stakes clinical settings. Such systems span post-hoc explainability (e.g., saliency maps, surrogate models), ante-hoc models built for interpretability (concept bottlenecks, prototype or rule-based architectures), and neuro-symbolic frameworks that integrate expert knowledge and data-driven learning.

1. Taxonomies and Principles of Interpretable AI in Medicine

Interpretable AI methods in medical diagnostics are classified according to when and how explanations are generated, their scope, and their modality (Lucieri et al., 2020). Ante-hoc methods impose explicit architectural constraints to yield explanations as part of model inference—examples include decision trees, concept-bottleneck models, prototype networks, and rule-based classifiers. Post-hoc approaches apply explanation techniques to trained black-box models, without modifying their internals; common strategies include visual attribution (Grad-CAM, saliency maps), surrogate models, and feature importance rankings (LIME, SHAP). Explanations may be local (per instance) or global (whole-model), and visual (highlighting pixels or regions) or textual (clinical reports, extracted rules).

Underlying these approaches are formal definitions of interpretability, including information-theoretic metrics such as normalized information gain from querying the decision process (Mukhopadhyay, 2018), and more heuristic measures such as fidelity (agreement between explanations and model behavior), localization accuracy, robustness, and clinical relevance.

2. Methodologies and Mathematical Foundations

Interpretability in medical diagnostics is realized via diverse mathematical and algorithmic frameworks:

• Saliency and Class Activation Maps: For a model output $y^c(x)$, saliency maps $\nabla_x y^c(x)$ quantify pixel-level influence. Grad-CAM defines per-channel weights $\alpha_k^c$ by averaging class gradients, producing a heatmap $$L^{c}_{\text{Grad-CAM}} = \mathrm{ReLU}(\sum_k \alpha_k^c A^k)$$ (Wen et al., 20 Sep 2025).
• Feature Attribution and Surrogate Models: Feature importance can be computed via permutation methods, ICE plots, or Shapley values. The Shapley value for feature $i$ is $$\phi_i = \sum_{S \subseteq N\setminus\{i\}} \frac{|S|!(|N|-|S|-1)!}{|N|!}[f_{S \cup \{i\}}(x) - f_S(x)]$$ (Karatza et al., 2022). Surrogate models fit interpretable decision trees or linear classifiers to approximate black-box decision boundaries.
• Concept Bottleneck Models: These models force predictions to depend exclusively on a vector of human-interpretable concept scores (e.g., clinical findings, expert-annotated attributes) (Rafferty et al., 2024, Wu et al., 2024). Prediction occurs in two stages: first, the model estimates concepts $c$, then uses $c$ to make a diagnosis $\hat{y}=f_{\text{class}}(c)$. This supports decomposability and offers built-in explanations.
• Prototype Networks and Case-Based Reasoning: Prototypical parts are learned and mapped to specific training patches, so every prediction can be decomposed into "this region looks like that previously seen case" (Barnett et al., 2021, Santos et al., 2024).
• Contrastive and Retrieval-Augmented Reasoning: ContrastDiagnosis computes the similarity between a query embedding and labeled support examples, presenting the closest analogues as a reasoning chain (Wang et al., 2024). Retrieval-augmented modules fuse query features with those of similar exemplars, weighted by attention, and generate localizations faithful to model decision-making (Urooj et al., 10 Dec 2025, Urooj et al., 20 Dec 2025).
• Neuro-Symbolic Integration: Recent systems embed clinical logic as weighted rules and atomic medical propositions, scoring their satisfaction and fusing this with neural predictions using confidence-weighted approaches. Adaptive routing selects the best expert branch per instance using entropy imbalance and rare-class metrics (Urooj et al., 5 Jan 2026).

3. Practical Applications and Empirical Results

Interpretable AI models have been deployed across imaging (mammography, CT, MRI, fundus photography), pathology, and multi-modal diagnostic domains:

• Breast Cancer Diagnosis: ICADx integrates an interpretable diagnosis network (IDN) and a synthetic lesion generator (SLGN) to map malignancy scores and BI-RADS descriptors, yielding both class predictions and visualizable lesion morphologies (Kim et al., 2018). Random Forests and neural networks explained by ICE plots and Shapley values achieved up to 97.18% accuracy (Karatza et al., 2022).
• Lung Nodule and Cancer Detection: Concept bottleneck models trained with expert-annotated radiological reports matched the diagnostic performance of black-box CNNs, while aligning explanations with clinical reasoning (Rafferty et al., 2024). ContrastDiagnosis leverages case-based reasoning to offer top-$k$ analogue pairs, achieving 98% accuracy and intuitive similarity-localization maps (Wang et al., 2024).
• Dementia Care and Multimodal Decision-Support: Hybrid models like ATHENA-CDS combine statistical evidence and clinician rules, supporting workflow fit and explanation by provenance (Kang et al., 2 Jul 2025). PEIRS, as an early rule-based system, remains a touchstone for transparency.
• Diabetic Retinopathy and Rare Disease: NEURO-GUARD and XAI-MeD fuse vision transformer features with explicit knowledge extraction and logical rule satisfaction for improved rare-class sensitivity and cross-domain generalization (Urooj et al., 20 Dec 2025, Urooj et al., 5 Jan 2026). Multimodal concept bottlenecks for choroid neoplasia diagnosis boosted junior clinician F1 by 42% (Wu et al., 2024).
• Chest Pathology and Critical Care: Interpretable boosted-tree classifiers for acute heart failure (AHF) used segmented CT biomarkers and SHAP explanations, matching radiologist-level ROC AUC and supporting stepwise review (Ørting et al., 11 Jul 2025). I-AI models explicitly learn radiologist attention patterns, linking dwell maps to diagnosis via vision-language prompting (Pham et al., 2023).

Interpretability metrics (AUC, F1, IoU, activation precision) are reported alongside explanation quality indicators (fidelity, localization overlap, user-study trust), confirming that interpretability can be achieved without significant loss of predictive accuracy.

4. Impact on Clinical Practice, Trust, and Regulation

The clinical impact of interpretable AI methods is evidenced by improved diagnostic accuracy, workflow efficiency, and user trust. Clinician-informed models (ClinicXAI, MMCBM) consistently provide explanations that match clinical expectations, outperforming post-hoc approaches that frequently diverge from expert assessment (Rafferty et al., 2024). User studies and application-grounded evaluations show that radiologists and clinicians report higher confidence and reduced decision time when supported by explainer visualizations (Grad-CAM, SHAP, prototype activations) (Lucieri et al., 2020, Wen et al., 20 Sep 2025). Junior clinicians, in particular, benefit from concept-based reasoning chains and transparent logic.

Regulatory requirements drive the adoption of interpretable designs. Legislation such as the EU AI Act mandates clear, auditable model reasoning. Best practices dictate regular fidelity audits, continuous performance monitoring, and embedding interpretation toggles or overlays directly into clinical workflows (EHR, PACS) (Wen et al., 20 Sep 2025).

5. Challenges, Limitations, and Future Research Directions

Despite successes, several challenges remain (Lucieri et al., 2020, Wen et al., 20 Sep 2025, Mandala, 2023):

• Ground Truth for Explanations: Lack of universal benchmarks for correct explanations; subjective variation in human interpretation.
• Robustness and Generalization: Saliency and concept predictions are sensitive to perturbations, input distributions, and class imbalance. Neuro-symbolic fusion serves as a buffer to domain shift, yet annotation cost and coverage of rule bases remain bottlenecks (Urooj et al., 5 Jan 2026).
• Computational Overhead: LIME, Kernel SHAP, and integrated gradients can be prohibitively slow for real-time diagnostics. Efficient surrogates and uncertainty quantification are active areas of research.
• Clinical Integration and Usability: Ensuring explanations do not increase cognitive load, cause alert fatigue, or foster blind model reliance requires human-centered design and rigorous workflow testing.
• Active Learning and Human-in-the-Loop: Deep active learning frameworks with prototype selection (ProtoAL), dynamic rule update, and clinician feedback expand the coverage of interpretable models while minimizing labeling effort (Santos et al., 2024, Kang et al., 2 Jul 2025).

The recommended directions include: standardized and multi-dimensional evaluation, expansion to multimodal and federated domains, automated mining and formalization of expert knowledge via LLMs, and continued regulatory engagement.

6. Summary Table: Key Interpretable AI Paradigms in Medical Diagnostics

Approach	Example Models / Techniques	Explanation Modality
Post-hoc Attribution	Grad-CAM, SHAP, LIME	Saliency heatmaps, feature bars
Ante-hoc Models	Concept bottleneck, prototype nets	Concept scores, case-based refs
Neuro-Symbolic	XAI-MeD, NEURO-GUARD	Rule satisfaction, logic traces
Hybrid Ensembles	PEIRS, ATHENA-CDS	Traceable rule and model mixes
Retrieval-based	MedXAI, ContrastDiagnosis	Similarity, top-$k$ analogues

Interpretable AI for medical diagnostics is a rapidly advancing field, balancing the demand for diagnostic precision with transparency, explainability, and regulatory compliance. Foundational architectures range from visual attributions and surrogate models to concept bottleneck and neuro-symbolic frameworks. Empirical evidence demonstrates parity or improvement in clinical metrics, enhanced trust, and practical workflow integration. Ongoing research is focused on multi-modal fusion, dynamic active learning, standardized benchmarks, and deeper human-in-the-loop collaboration for the next generation of safe and actionable medical AI.