Explainable AI in Healthcare: A Comparative Analysis of Interpretability Techniques for Clinical Decision Support Systems
Riya Jacob K
Aug 2026· International Journal of Technology and Emerging Research· 0 citations· 16 references
TL;DR
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.
Abstract
Artificial intelligence has made a great impact on healthcare by providing accurate disease diagnosis, personalised treatment regimens, and efficient clinical decision making. But many of the advanced machine learning and deep learning models are black-box systems, and healthcare professionals find it difficult to understand the logic behind their predictions. This opacity hinders the adoption of intelligent systems in clinical settings where trust and accountability are a must. In this review paper we compare the main interpretability techniques that have been used in clinical decision support systems. These techniques include Local Interpretable Model-Agnostic Explanations (LIME), SHapley Additive exPlanations (SHAP), saliency maps, Gradient-weighted Class Activation Mapping (Grad-CAM), attention mechanisms, and decision trees, among others. We performed a systematic literature review to evaluate these techniques based on interpretability, computational complexity, scalability, transparency, and clinical relevance. A systematic literature review was performed to evaluate the techniques in terms of interpretability, computational complexity, scalability, transparency and clinical relevance. The analysis shows that SHAP provides complete local and global explanations, while LIME provides computationally efficient local interpretations. Visualisation based methods such as Grad-CAM and saliency maps are especially useful for medical image analysis, while attention mechanisms are suitable for sequential healthcare data. The study concludes 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.
Keywords: machine learning; clinical decision support systems; Explainable Artificial Intelligence; Healthcare Analytics; interpretability
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
Most artificial intelligence (AI) models used in radiology are black boxes-they produce predictions without explaining the basis of their outputs, raising concerns about clinical safety, accountability, and trust. To address this, a growing body of methods has been developed to help clinicians understand and evaluate AI predictions. This field, known as explainable AI (XAI), aims to help clinicians interrogate, interpret, and critically evaluate AI predictions by identifying factors associated with model outputs. In this educational and practical review, we provide an accessible overview of XAI tailored for practicing radiologists and physicians. We cover the major categories of explanation methods, including saliency maps, perturbation-based and feature-attribution approaches, concept- based methods, and example-based reasoning, as well as uncertainty quantification as a complementary approach for assessing prediction reliability, along with common misconceptions and emerging regulatory obligations. We aim to make XAI easier for healthcare professionals to understand, as effective oversight of AI tools has become a core competency for the modern radiologist.
Gorkem Durak, H. Aktas, Tugba Akinci D’Antonoli et al.· Diagnostic and Interventiona...· 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
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
This systematic review provides a comprehensive analysis of XAI methods specifically applied to tabular healthcare data for classification tasks, revealing that SHAP remains the dominant post-hoc method, achieving strong model fidelity but showing inconsistent alignment with clinical expert reasoning.
Angelower Santana-Velásquez, M. B. Salazar-Sánchez· Computers· 0 citations
This study aims to illustrate the significance of Abstract Science Intelligence (XAI) in the medical field, outlining its role in enhancing the interpretability of traditional "black-box" AI models.The goal of this research is to shed light on the significance of Abstract Science Intelligence (XAI) in the medical sector, elucidating its function in bolstering the interpretability of conventional "black-box" Artificial Intelligence models. In medical imaging, disease diagnosis, clinical decision support, and precision medicine, deep learning techniques have proven to be extremely successful; however, the inability to explain and understand how deep learning models work raises a variety of challenges for clinician trust, patient safety, ethical accountability, and regulatory compliance. This review provides a comprehensive overview of recent developments in XAI for healthcare, focusing on key methods for achieving interpretability, such as intrinsically interpretable models, and post-hoc explanation methods such as SHAP, LIME, Grad-CAM, attention mechanisms, surrogate models, and counterfactual explanations. A detailed review of the use of these methods in a variety of clinical areas such as radiology, oncology, cardiology, genomics, electronic health records and drug discovery is also given. Furthermore, the paper examines technical issues concerning explanation fidelity, computational complexity, robustness, scalability, and model validation, as well as ethical issues such as fairness, transparency, privacy, bias, and governance. Other research trends are also discussed, such as causal explainability, humancentered XAI, models that are uncertain, human-in-the-loop systems, and evaluation frameworks. In summary, the review highlights that explainability is not just a technical aspect but a key component in creating AI systems that are both trustworthy and clinically sound and can assist in safe and effective health care decision-making.
S. R. Ali, .a. Meher Nisha, K. A. Sathik et al.· International Journal of Int...· 0 citations
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