Jul 2026· International Conference on Smart Communications and Networking· pp. 1-6· 0 citations· 19 references
Abstract
The development of AI models in healthcare AI is hampered by a central dilemma of improving both requirements of performance and interpretability in healthcare systems. Some AI models (e.g., “XGBoost (eXtreme Gradient Boosting)”) offer remarkable accuracy, but their predictions are difficult to explain to clinicians for treatment validation. The interpretability of AI models in healthcare is crucial to meet regulatory constraints (e.g., General Data Protection Regulation (GDPR), Health Insurance Portability and Accountability Act (HIPAA)), which mandate traceability of automated decisions. This paradox is compounded by the limitations of existing explainability methods (SHAP, LIME), which suffer from instability and often produce explanations that are too technical for clinical workflows. The central problem, therefore, is to design a methodology that preserves predictive effectiveness while meeting the practical needs of clinical environments. We design an ontology-based healthcare model to improve both attributes of performance and interpretability.
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
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 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 review examines current applications of LLMs to health economic modeling in terms of reproducibility, validation, adaptability, and technology readiness and concludes that near-term gains are augmenting human modelers on decomposed, verifiable sub-tasks rather than pursuing autonomous end-to-end modeling.
Attila Imre, B. Németh, Á. Jóźwiak et al.· Expert review of pharmacoeco...· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
