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

The gap

Consensus across the literature

Supporting evidence — 5 representative gaps

• Explainable Deep Learning Framework for Breast Cancer Classification (2026) · 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.

  Keywords: Grad-CAM CNN explainable AI interpretability SHAP feature importance breast cancer classification
• Diagnostic Performance of Artificial Intelligence and Deep Learning for Diabetic Retinopathy Screening: A Systematic Review and Meta-analysis (2026) · doi

  Explainable AI techniques that provide clinicians with interpretable reasoning for diabetic retinopathy classifications have not been systematically developed and validated, limiting trust and effective human-AI collaboration in screening workflows.

  Keywords: diabetic retinopathy deep learning explainable AI interpretability trust human-AI collaboration
• Enhancing Tuberculosis Detection from Chest X-Ray Images Using Deep Learning: Evaluating Multi-Architecture Performance and Efficiency (2026) · 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.

  Keywords: explainable AI XAI tuberculosis detection chest X-ray interpretability clinical trust visualization
• Multimodal artificial intelligence in urologic precision oncology: from algorithm to translational medicine (a systemized narrative review) (2026) · doi

  Explainable AI approaches have been applied to decode pan-cancer treatment outcomes using multimodal real-world data (reference 37), but the specific interpretability requirements and validation methods for clinical adoption of explainable AI in prostate cancer precision oncology decision-making have not been standardized.

  Keywords: explainable artificial intelligence prostate cancer multimodal real-world data interpretability clinical validation
• Decoding Minds through Machines: A Transformer-Driven Deep Learning Framework for Mental Health Text Classification (2026) · doi

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

  Keywords: framework requires exploration explainable techniques improve model interpretability clinical deployment