ARTIFICIAL INTELLIGENCE APPLICATIONS IN CLINICAL DATA SYSTEMS: A COMPARATIVE EMPIRICAL ANALYSIS OF CURRENT APPROACHES AND FUTURE DIRECTIONS

Artificial Intelligence (AI) is now being integrated into clinical data systems, transforming the capture, analysis, and action on health information at the point of care. This paper presents an empirical, comparative study on the use of AI methodologies for structured and unstructured clinical data, summarizing quantitative evidence of performance from 148 peer-reviewed publications spanning the years 2015–2022. The six major families of techniques were identified: supervised machine learning, deep neural networks, natural language processing, computer vision, reinforcement learning, and federated learning, and comparative analyses of the diagnostic performance of these techniques were performed for four representative clinical tasks: risk stratification, diagnostic classification, clinical note summarization, and readmission prediction. The results show that the transformer-based natural language processing models and the convolutional deep networks consistently perform better than the traditional machine learning baselines, with mean accuracies of 90.4% and 88.5%, respectively, versus 77.6%-81.9% for logistic regression, random forest, and support vector machine approaches. The analysis also highlights existing challenges for clinical translation, such as data interoperability constraints, algorithmic bias, lack of model interpretation and unresolved regulatory validation. In light of these findings, the paper calls for a translational approach that considers federated and privacy-preserving learning, standardized interoperability protocols and integration of explainable-AI as key priorities for the next generation of clinical data systems. The results provide evidence-based recommendations for informaticians, health system leaders, and policy makers who are looking to scale carefully AI-powered clinical data systems.