AI-Driven Smart Radiology for Early Disease Detection and Treatment Outcome Prediction in Personalized Medicine

The paradigm of medical imaging is shifting from qualitative visual interpretation to quantitative computational analysis. This study introduces a novel Smart Radiology framework that integrates High-Dimensional Radiomics (HDR) with Multi-modal Deep Learning (MDL) to enhance early disease detection and treatment trajectory prediction. Utilizing a dataset of ۵,۰۰۰ heterogeneous clinical cases, we developed a hybrid architecture combining Vision Transformers (ViTs) and ۳D Convolutional Neural Networks (۳D-CNNs). Our framework extracts sub-visual phenotypic features to identify stage-۰ malignancies and early neurodegenerative biomarkers. The results demonstrate a significant leap in diagnostic sensitivity (۹۴.۲%) and a robust predictive capability for therapeutic response (۸۸.۵%). This research provides a scalable blueprint for integrating AI into personalized clinical workflows, facilitating precision intervention and reducing healthcare costs.

کلیدواژه ها:

Smart Radiology ، Artificial Intelligence ، Personalized Medicine ، Radiomics ، Early Detection ، Predictive Analytics