Jul 2026· European International Journal of Multidisciplinary Research and Management Studies· 0 citations
TL;DR
This review critically examines the role of Explainable Artificial Intelligence in developing trustworthy Clinical Decision Support Systems by synthesizing recent advances in artificial intelligence architectures, trustworthy computing principles, healthcare automation, cloud intelligence, workflow optimization, cybersecurity, and distributed system reliability.
Abstract
The increasing integration of Artificial Intelligence (AI) into healthcare has transformed Clinical Decision Support Systems (CDSS) from rule-based advisory platforms into intelligent systems capable of diagnosing diseases, predicting clinical outcomes, recommending treatments, and optimizing healthcare workflows. Despite remarkable improvements in predictive performance through deep learning, ensemble learning, and generative artificial intelligence, the widespread clinical adoption of AI remains constrained by the limited transparency of complex machine learning models. Most high-performing AI algorithms function as "black-box" systems, providing highly accurate predictions while offering minimal explanation regarding the reasoning behind clinical recommendations. This lack of interpretability raises significant concerns regarding physician trust, patient safety, accountability, regulatory compliance, and ethical responsibility. Explainable Artificial Intelligence (XAI) has consequently emerged as a fundamental research direction aimed at improving transparency without substantially compromising predictive performance.
This review critically examines the role of Explainable Artificial Intelligence in developing trustworthy Clinical Decision Support Systems by synthesizing recent advances in artificial intelligence architectures, trustworthy computing principles, healthcare automation, cloud intelligence, workflow optimization, cybersecurity, and distributed system reliability. The review develops a comprehensive conceptual framework integrating explainability, clinical validation, fairness, privacy preservation, workflow automation, and governance into a unified trust model for intelligent healthcare environments. Existing research demonstrates that explainability improves physician confidence, facilitates regulatory acceptance, enhances diagnostic validation, and enables collaborative human-AI decision making. Simultaneously, emerging technologies including federated learning, digital twins, cloud intelligence, workflow automation, and real-time monitoring significantly strengthen scalability and operational resilience within healthcare infrastructures.
A reproducible experimental approach to developing and evaluating explainable AI systems for healthcare analytics that integrates the steps of data preprocessing, predictive modeling, interpretation generation, and evaluation into one seamless workflow that can be applied to both structured clinical data and medical imaging datasets.
Yashwant Dongre, Deepali A. Godse, Prawit Chumchu et al.· Journal of Visualized Experi...· 0 citations
Artificial intelligence (AI) has become an integral component of clinical decision support systems, improving diagnostic accuracy, risk prediction, treatment planning, and healthcare resource management. But, the "black box" nature of many successful machine learning models has brought up concerns around trust and accountability, fairness and regulatory acceptance in the clinical setting. To address these challenges, Explanatory Artificial Intelligence (XAI) has become a promising method that aims to provide the explainability of the results predicted by the models without compromising the performance of the analysis. The aim of this narrative review is to provide an overview of the evolving image of XAI as a tool to create trustworthy clinical decision support systems and how it can be performed based on the concepts of transparency, interpretability and moral decision-making. It provides a summary of the current literature on essential explainability techniques, how they can be applied to different health care-related problems, and how they help increase trust and transparency in health care decision making. It also addresses human-AI collaboration, model validation, bias mitigation, privacy protection and governance frameworks to enable responsible use of AI. The new emerging developments, such as federated learning, multimodal explainable models, causal reasoning, and generative AI, are also discussed to emphasize future opportunities for clinically reliable and scalable intelligent healthcare systems. The review concludes that explainability is no longer a choice of technical attribute but rather a fundamental component to the use of AI in everyday clinical practice. For safe, equitable and trusted clinical decision support to benefit everyone in the health care sector, transparency, accountability and ethical governance will play a pivotal role.
Rakesh Venuturumilli, Amoli Singh, Hemanshi Dhaduk et al.· European Journal of Prosthod...· 0 citations
It is concluded that explainable artificial intelligence improves trust, reliability, and accountability in healthcare systems and is a prerequisite for successful integration of intelligent technologies into clinical practice.
Riya Jacob K· International Journal of Tec...· 0 citations
A methodology is advanced to embed explainability in the AI decision-making process, starting from data preprocessing to generating explanations and human evaluation, and the results highlight the potential of explainability to enhance human comprehension and foster responsible use of AI systems in high-stakes decision-making scenarios.
Mahabala H. N.· International Journal of Mod...· 0 citations
Explainable Artificial Intelligence (XAI) has become a key research focus nowadays due to the growing use of more intricate machine learning and deep learning systems in high-stakes systems. Although contemporary artificial intelligence (AI) methods show impressive prediction accuracy, the lack of transparency, a characteristic of their opaque (black-box) essence, presents serious forestalling issues in the areas of transparency, trust, accountability, and regulatory compliance. This interpretability is a disadvantage as numerous areas, like healthcare, finance, autonomous systems, and governance of the people, need AI systems to be applied in areas that are sensitive and require decision-making in a way that is comprehensible and explainable to human participants. XAI aims to solve these dilemmas by creating approaches and systems that allow human operators to comprehend, trust, and be able to handle AI-motivated decisions. XAI is not only aimed at providing explanations, but also at making these explanations meaningful, faithful to underlying model and applicable by various groups of users such as domain experts, developers, and policymakers. Enabling transparency, XAI leads to ethical AI, reduces bias and enhances debugging and model checking, and enables compliance with the developing regulatory frameworks like the General Data Protection Regulation (GDPR). This paper constitutes a thorough discussion of the XAI, as applied on transparent decision systems. It starts with a general introduction to motivation and the conceptualization of explainability in AI and goes on to provide a comprehensive literature review of model-specific and model-agnostic explainability algorithms. The suggested methodology combines both local and global explanatory approaches and transparency leadership framework. Experimental findings show the effectiveness of XAI techniques to enhance interpretability without causing a major loss in predictive accuracy. Lastly, the paper provides the practical implications, limitations, and research directions on the future of explainable and trustworthy AI systems.
Unknown authors· International Journal of App...· 0 citations
A framework through XAI to incorporate various health data sources such as electronic health records, medical imaging, laboratory reports, and wearable sensor information, which can be integrated in the context of achieving higher predictive performance in disease prediction and treatment stratification, as well as decision-making transparency.
M. Aparna, S. Lahane, Dr. Bharti A. Dixit· Journal of Intelligent Decis...· 0 citations
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