Explainable Artificial Intelligence (XAI) for Clinical Decision Support Systems

Abstract

Background: Artificial Intelligence (AI) has become increasingly integrated into healthcare, particularly in Clinical Decision Support Systems (CDSS) that assist clinicians in diagnosis, prognosis, and treatment planning. Despite their predictive power, many AI models—especially deep learning algorithms—operate as “black boxes,” limiting clinicians’ trust and acceptance. Explainable Artificial Intelligence (XAI) has emerged as a promising approach to enhance transparency and interpretability in AI-driven healthcare systems.

Objective: This study aims to examine the role of Explainable Artificial Intelligence techniques in improving the transparency, interpretability, and clinical adoption of AI-based Clinical Decision Support Systems.

Methods: A systematic analysis of existing XAI approaches used in clinical decision support will be conducted. The study will evaluate commonly used interpretability techniques such as SHAP (Shapley Additive Explanations), LIME (Local Interpretable Model-Agnostic Explanations), and attention-based mechanisms in machine learning models applied to healthcare datasets. Performance metrics, interpretability measures, and clinician usability will be assessed to determine the effectiveness of XAI methods.

Results: The analysis is expected to demonstrate that integrating XAI techniques into clinical decision support systems significantly improves model interpretability and clinician trust without substantially compromising predictive performance. Explainable models are anticipated to facilitate better understanding of feature importance and clinical reasoning behind AI predictions.

Conclusion: Explainable Artificial Intelligence has the potential to bridge the gap between complex machine learning models and clinical decision-making by providing transparent and interpretable insights. The adoption of XAI-enabled CDSS may enhance trust, accountability, and integration of AI technologies into clinical practice.