Explainable AI Models for Clinical Decision Support Systems
DOI:
https://doi.org/10.66280/cis.v1i1.258Keywords:
Explainable artificial intelligence; clinical decision support systems; healthcare analytics; interpretable machine learning; medical informatics; healthcare governance; algorithmic transparency; trustworthy AI; healthcare infrastructure; human-centered AIAbstract
Clinical decision support systems have undergone substantial transformation with the integration of artificial intelligence technologies into diagnostic, prognostic, and therapeutic workflows. While machine learning and deep learning systems have demonstrated remarkable predictive capabilities in domains such as radiology, oncology, pathology, intensive care management, and personalized medicine, the lack of interpretability in many advanced models has generated significant concerns regarding transparency, accountability, safety, and regulatory compliance. Explainable artificial intelligence has emerged as a critical interdisciplinary framework designed to address these limitations by enabling clinicians, healthcare administrators, and patients to better understand the reasoning processes underlying algorithmic recommendations. This paper examines the architectural foundations, governance structures, operational challenges, and socio-technical implications associated with explainable AI models in clinical decision support systems. The study analyzes the evolution of explainability methodologies across symbolic systems, probabilistic frameworks, and neural network-based architectures while evaluating the trade-offs between model performance, interpretability, scalability, and clinical usability. Particular attention is devoted to fairness, robustness, human-centered interaction, infrastructure integration, and regulatory oversight in healthcare environments characterized by heterogeneous data sources and high-stakes decision making. The paper further explores deployment challenges across hospital ecosystems, including interoperability, clinician trust calibration, workflow adaptation, cybersecurity, and sustainability concerns. Through cross-domain analysis and examination of emerging institutional practices, the study argues that explainability should not be treated solely as a technical property but rather as an organizational and governance capability embedded within broader healthcare infrastructures. The paper concludes by outlining future research directions focused on collaborative intelligence, adaptive interpretability frameworks, and responsible AI governance models capable of supporting resilient and equitable healthcare systems.
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