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March 1, 2026Open Access

Artificial Intelligence in Clinical Decision Support Systems: Architectures, Applications, and Ethical Challenges

Key Points

Examine the integration of artificial intelligence in clinical decision support systems and their architectural evolution.
Conduct a structured review of AI-powered clinical decision support systems.
Analyze historical evolution, enabling technologies, and ethical considerations.
Assess predictive modeling and patient stratification techniques used in AI-CDSS.
Identified the limitations of traditional rule-based CDSS in managing multimorbidity and personalized care.
Highlighted advancements in AI methodologies such as machine learning and natural language processing.
Discussed ethical and legal challenges alongside emerging research directions like federated learning and digital twins.

Abstract

Abstract: Clinical Decision Support Systems (CDSS) assist healthcare professionals in making timely, accurate, and evidence-based clinical decisions. The increasing volume, velocity, and heterogeneity of healthcare data have exposed the limitations of traditional rule-based CDSS, particularly in managing multimorbidity and personalized care. Intelligent CDSS capable of adaptive learning, predictive modeling, and patient stratification has been enabled by the recent advances in Artificial intelligence which include machine learning, deep learning, and natural language processing (NLP), This paper presents a structured and system-level review of AI-powered CDSS, focusing on their historical evolution, enabling technologies, architectural design, and clinical applications. The surveys conducted earlier emphasized isolated algorithms, whereas this review integrates AI techniques with system architecture, workflow design, and ethical considerations. Key AI approaches for patient stratification, deep learning models for diagnosis and prognosis, and NLP-based early warning systems are examined. Ethical, legal, and explainability challenges are critically discussed, and emerging research directions such as federated learning, digital twins, and genomic CDSS are highlighted. The paper aims to provide researchers and clinicians with a comprehensive understanding of AI-CDSS design principles and future potential. Keywords: Clinical Decision Support Systems, Artificial Intelligence, Deep Learning, Patient Stratification, Explainable AI. Title: Artificial Intelligence in Clinical Decision Support Systems: Architectures, Applications, and Ethical Challenges Author: Deepika D Pai, Ramya R, Inchara M, H Sunil International Journal of Engineering Research and Reviews ISSN 2348-697X (Online) Vol. 14, Issue 1, January 2026 - March 2026 Page No: 26-31 Research Publish Journals Website: www.researchpublish.com Published Date: 25-February-2026 DOI: https://doi.org/10.5281/zenodo.18769791 Paper Download link (Source) https://www.researchpublish.com/papers/artificial-intelligence-in-clinical-decision-support-systems-architectures-applications-and-ethical-challenges

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Pai et al. (Wed,) studied this question.

synapsesocial.com/papers/69a3d8b8ec16d51705d2fe22 https://doi.org/https://doi.org/10.5281/zenodo.18769791

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