NLP and Explainable AI for Clinical Decision Support: A Comprehensive Review with Emerging Quantum Perspectives

Abstract

Clinical Decision Support (CDS) systems play a fundamental role in increasing healthcare distribution by providing clinicians with evidence-based perceptions for timely and precise decision-making. As healthcare data grows progressively formless and huge, Natural Language Processing (NLP) has appeared as a critical tool for extracting meaningful clinical information from electronic health records, medical literature, and patient accounts. However, the black-box nature of many NLP-driven systems increases concerns about transparency, trust, and interpretability, making Explainable AI (XAI) important for developing clinician confidence and regulatory compliance. This review provides a comprehensive investigation of the current landscape of NLP and XAI techniques in the context of Clinical Decision Support (CDS), examining their methodologies, applications, and limitations. Also, it introduces the emerging possibility of quantum computing to address the computational and scalability contests of clinical NLP, highlighting current quantum-enhanced approaches. The review also classifies noteworthy research gaps, counting the need for clinically relevant explainability, standardized evaluation frameworks, and the practical mixing of quantum tools. By creating insights across these domains, the paper summarizes a roadmap for the next generation of brainy, transparent, and scalable clinical decision support systems.

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