The framework requires exploration of explainable AI techniques to improve model interpretability for clinical deployment

The gap

Evidence profile

Sourced from the open questions and future work of the source papers, classified as methodology gap, spanning 5 journals.

Supporting evidence — 5 representative gaps

• Explainable Deep Learning Framework for Breast Cancer Classification (2026) · International Journal for Research in Applied Science and Engineering Technology · doi

  The Grad-CAM explainability technique is described as providing only 'moderate interpretability' for CNN models in breast cancer classification, whereas Random Forest and SVM achieve 'high interpretability' through feature importance and decision boundaries. The paper does not investigate methods to enhance Grad-CAM interpretability or compare it directly with other XAI techniques like SHAP beyond mention, leaving a gap in determining which explainable AI method optimally balances accuracy (97.6% CNN) with clinician interpretability.

  methodology gapopen questionsevidence 5/5
  Keywords: Grad-CAM CNN explainable AI interpretability SHAP feature importance breast cancer classification
• Enhancing Predictive Performance under Data Scarcity: The Role of Generative AI-Based Synthetic Data (2026) · Research Digest on Engineering Management and Social Innovations · doi

  The integration of explainable AI with generative models for synthetic data interpretation requires concrete architectural specifications; the paper proposes hybrid ecosystems blending generative models with XAI to demystify predictions but does not detail which explanation methods (SHAP, LIME, attention mechanisms) should be evaluated for transparency in healthcare personalization or PdM scheduling contexts.

  methodology gapfuture workevidence 5/5
  Keywords: explainable AI generative models synthetic data interpretability SHAP LIME attention mechanisms
• Enhancing Tuberculosis Detection from Chest X-Ray Images Using Deep Learning: Evaluating Multi-Architecture Performance and Efficiency (2026) · Engineering, Technology & Applied Science Research · doi

  The paper explicitly calls for integration of explainable AI (XAI) techniques to strengthen clinical trust in TB detection models but does not specify which XAI methods (attention visualization, LIME, SHAP) should be applied to VGG16 or MobileNetV2, nor does it propose validation metrics for evaluating explanation quality or clinical interpretability.

  methodology gapfuture workevidence 5/5
  Keywords: explainable AI XAI tuberculosis detection chest X-ray interpretability clinical trust visualization
• Enhancing Breast Cancer Diagnosis through Machine Learning: A Robust Approach for Early Detection (2026) · International Journal of Information Engineering and Electronic Business · doi

  The machine learning models lack explainability mechanisms to help healthcare professionals understand feature contributions to breast cancer predictions. Implementing Explainable AI (XAI) techniques to increase model transparency and enable clinicians to interpret Random Forest and XGBoost predictions is identified as necessary but not yet integrated into the diagnostic system.

  methodology gapfuture workevidence 5/5
  Keywords: Explainable AI XAI model interpretability Random Forest feature importance healthcare transparency
• Decoding Minds through Machines: A Transformer-Driven Deep Learning Framework for Mental Health Text Classification (2026) · International Journal of Mathematics And Computer Research · doi

  The framework requires exploration of explainable AI techniques to improve model interpretability for clinical deployment.

  methodology gapfuture workevidence 4/5
  Keywords: framework requires exploration explainable techniques improve model interpretability clinical deployment

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