ARTIFICIAL INTELLIGENCE DRIVEN CLINICAL DECISION SUPPORT SYSTEMS: OPPORTUNTIES, FUTURE AND DIRECTIONS

The rapid growth of healthcare data from heterogeneous sources such as electronic health records (EHRs), medical imaging, laboratory reports, and wearable sensors has created both opportunities and challenges for clinical decision-making. Traditional clinical decision support systems (CDSS) often rely on rule-based approaches that struggle to effectively integrate and interpret complex multimodal data. This study proposes a multimodal artificial intelligence–based clinical decision support system that leverages machine learning and knowledge extraction techniques to enhance diagnostic accuracy, treatment recommendations, and clinical workflow efficiency. The proposed system integrates structured and unstructured healthcare data using advanced preprocessing, feature fusion, and representation learning methods. Machine learning models such as supervised classifiers and deep learning architectures were used to analyze patterns across modalities such as clinical text, imaging, and physiological signals. The framework is designed with an interoperable architecture that can interface with existing hospital information systems and support real-time inference. The implementation was carried out using Object-oriented analysis and design methodology (OOADM) and experimental evaluation on benchmark clinical datasets demonstrates that the multimodal approach significantly outperforms unimodal models in predictive accuracy, sensitivity, and specificity. The system achieved 95% accuracy in prediction. The system also improves interpretability through knowledge-driven explanations, thereby increasing clinician trust and usability. The findings suggest that integrating machine learning with knowledge extraction in a multimodal CDSS can substantially improve early diagnosis, risk stratification, and personalized treatment planning.