Enhancing Clinical Decision Support Systems through Explainable AI (XAI): A Framework for Risk Mitigation and Transparency

• Author(s): R. Raghavendra; S. Jesbert
• Paper ID: 1717950
• Page: 2674-2679
• Published Date: 19-05-2026
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 9 Issue 11 May-2026

Abstract

As the healthcare industry transitions toward the Healthcare 5.0 paradigm [10], deep learning models have demonstrated superior predictive performance across critical clinical domains, including oncology [5], cardiology [1, 7], neurology [11, 15], and behavioral health [6]. However, the clinical adoption of these high-performing systems remains obstructed by the "black-box" nature of deep learning, which lacks the transparency required for high-stakes medical decision-making [10]. While post-hoc Explainable AI (XAI) techniques like SHAP, LIME, and Grad-CAM are increasingly deployed to provide interpretability, this study identifies a significant gap: current XAI outputs are often unstable under extreme class imbalance (e.g., in sepsis detection) [2], sensitive to variations in imaging hardware (e.g., MRI Tesla strength) [11], and frequently disconnected from established biomedical knowledge [8]. This research provides a comprehensive systematic review and framework development based on 15 recent studies to address these limitations. The proposed methodology outlines a multi-modal approach that integrates visual feature localization (e.g., PSPNet and DenseNet-121) [14] with Retrieval-Augmented Generation (RAG) and Large Language Models (LLMs) [9] to transform raw heatmaps into verified, natural language diagnostic reports. Furthermore, the study introduces a "Clinical Utility Metric" grounded in Social Science theories and Biomedical Knowledge Graphs [8] to mathematically score the relevance of AI explanations for human practitioners. Key objectives include evaluating the stability of predictors across demographic-balanced datasets [12] and implementing a "Self-Correction Layer" to mitigate the risk of medical hallucinations in AI-generated text. The expected contributions of this work include the identification of "Universal Predictors" for heart failure and stroke [1, 13] that remain consistent across multi-center electronic health records (EHR), and a standardized benchmark for evaluating XAI in a medical-legal context. Ultimately, this framework aims to bridge the gap between technical faithfulness and clinical trust, ensuring that AI acts as a reliable "second opinion" that enhances patient safety and reduces clinician cognitive load

Keywords

Explainable AI, Clinical Decision Support Systems, Deep Learning, Healthcare 5.0, Multi-modal Interpretability, Model Transparency, Human-Centered AI

Citations

IRE Journals:
R. Raghavendra, S. Jesbert "Enhancing Clinical Decision Support Systems through Explainable AI (XAI): A Framework for Risk Mitigation and Transparency" Iconic Research And Engineering Journals Volume 9 Issue 11 2026 Page 2674-2679

IEEE:
R. Raghavendra, S. Jesbert "Enhancing Clinical Decision Support Systems through Explainable AI (XAI): A Framework for Risk Mitigation and Transparency" Iconic Research And Engineering Journals, 9(11)