Leveraging Deep Learning Models to Enhance Clinical Decision Support Systems in Healthcare

Abstract

CDSS, or clinical decision support systems, have transformed healthcare practices by integrating deep learning models that improve the accuracy of diagnosis, predictive analytics, and personalized treatment plans. This research evaluates the extent to which deep learning technologies, such as Transformer models, Recurrent Neural Networks (RNNs), and Convolutional Neural Networks (CNNs), can enhance clinical decision-making with datasets like Electronic Health Records (EHRs) and medical imaging. The suggested framework demonstrates 91% accuracy, 90% precision, and 89% recall under real-world clinical environments. Key methodologies involve transfer learning, representation learning, and connectionism that enable CDSSs to obtain actionable information from vast amounts of unstructured clinical data. Further, Explainable AI (XAI) methods such as SHAP and LIME are employed to enhance model transparency and trustworthiness, enhancing clinical acceptability. Although there has been progress, issues persist, such as data privacy and regulatory challenges. The findings illustrate that deep learning models significantly enhance diagnostic accuracy, particularly in radiology and cardiology, and have promise for personalized treatment. For instance, by accurately detecting arrhythmias, deep learning-based ECG analysis reduced sudden cardiac deaths. Widespread adoption is still hindered by challenges such as integration, model interpretability, and clinician adoption. To overcome these challenges and to maximize the use of deep learning in clinical decision support, the conclusion of the paper emphasizes the need for continuous research and interdisciplinary collaboration.