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Enhancing Clinical Decision Support Systems through Explainable AI (XAI): A Framework for Risk Mitigation and Transparency
Subject area: Biological & Medical Sciences · Area of research: Clinical Decision Support System
DOI: https://doi.org/10.64388/IREV9I11-1717950
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
References
[1] Parthasarathy, S., Jayaraman, V., & Abishek, S. (2025). A High-Precision Clinical Decision Support System for Heart Failure Prediction using HF-PGANN Model. ICSSAS-2025.
[2] Chakraborty, S., et al. (2023). An Explainable AI based Clinical Assistance Model for Identifying Patients with the Onset of Sepsis. IEEE IRI.
[3] Kyparissidis-Kokkinidis, I., et al. (2024). An Explainable AI-Based Decision Support Tool to Predict Preterm Birth. IEEE BHI.
[4] Mandava, R., et al. (2025). An In-Depth Study on the Integration of Explainable AI Techniques to Enhance Interpretability in Clinical Risk Prediction Models. ICNSoC.
[5] Srinidhi B., & Bhargavi, M. S. (2023). An XAI Approach to Predictive Analytics of Pancreatic Cancer. ICIT.
[6] Zhang, T., et al. (2025). AXAI-CDSS: An Affective Explainable AI-Driven Clinical Decision Support System for Cannabis Use. ABC.
[7] Torquati, M. C., et al. (2025). Development of an Explainable-AI Enabled Decision Support System for Improved Risk Assessment of Atrial Fibrillation. IEEE EMBC.
[8] Ghanvatkar, S., & Rajan, V. (2024). Evaluating Explanations From AI Algorithms for Clinical Decision-Making: A Social Science-Based Approach. IEEE JBHI.
[9] Liu, Y.-K., & Tsai, Y.-C. (2024). Explainable AI for Trustworthy Clinical Decision Support: A Case-Based Reasoning System for Nursing Assistants. IEEE Big Data.
[10] Volkov, E. N. (2023). Explainable Artificial Intelligence in Clinical Decision Support Systems. NeuroNT.
[11] Alrougi, E., & Meshref, H. (2025). Leveraging Early Diagnosis of Alzheimer's Disease Using Deep Learning and XAI. FICAC25.
[12] Ahuja, R. (2024). Leveraging XAI for Discovering Crucial Demographic, Clinical and Pathological Diabetes Mellitus Biomarkers. DELCON.
[13] Ugbomeh, O., et al. (2024). Machine Learning Algorithms for Stroke Risk Prediction Leveraging on XAI. ICEECT.
[14] Haitham, M., & Sharaf, N. (2025). XAI Meets Radiology: Localized Chest X-ray Diagnosis with Natural Language Explanations. IV.
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How to cite this paper
@article{1717950,
author = {R. Raghavendra, S. Jesbert},
title = {Enhancing Clinical Decision Support Systems through Explainable AI (XAI): A Framework for Risk Mitigation and Transparency},
journal = {Iconic Research And Engineering Journals},
year = {2026},
volume = {9},
number = {11},
pages = {2674-2679},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1717950.pdf},
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},
month = {May},
doi = {https://doi.org/10.64388/IREV9I11-1717950}
}