Model for AI-Enhanced Disease Diagnosis and Prognosis

Abstract

Artificial intelligence (AI) is rapidly transforming healthcare by enabling advanced data integration and predictive modeling for improved diagnosis and prognosis. This study developed a conceptual AI-enhanced diagnosticprognostic framework that combines multimodal data-including electronic health records, laboratory results, imaging, and genomicsusing supervised machine learning, deep neural networks, and natural language processing. Retrospective datasets with complete diagnostic and follow-up information from oncology, cardiology, and infectious diseases were included. The model outperformed traditional physicianonly diagnostic approaches, demonstrating higher accuracy and stronger predictive performance. Notably, AI improved early cancer detection accuracy to 93% (vs. 82% conventionally), enhanced cardiovascular prognostic prediction by 12%, and increased mortality prediction accuracy in infectious diseases by 9%. Sensitivity, specificity, and AUROC results further supported model reliability. Overall, the findings suggest that integrating multimodal data with advanced AI methods can substantially reduce diagnostic errors, strengthen prognostic assessments, and support scalable clinical decision-making, warranting further prospective validation.

Ähnliche Arbeiten