AI-Driven MRI Analysis: Disease Prediction and Report Generation

Abstract

Magnetic resonance imaging is a widely used non-invasive imaging technique for identifying and diagnosing a variety of diseases. Nevertheless, the conventional manual interpretation of Magnetic resonance imaging scans is time-consuming, labor-intensive, and susceptible to inter-observer variability. Because of the increasing volume of imaging data, radiologists need instruments for artificial intelligence that can help them spot abnormalities, predict illnesses, and generate accurate and efficient diagnostic reports. This research offers a framework powered by artificial intelligence that combines automated diagnostic report generation with natural language processing. The system is designed to analyze identify disease-related features, predict possible conditions, and generate diagnostic summaries that are fact-based and well-organized. The produced reports reduce the diagnostic burden and are consistent with clinical standards, and experimental evaluations indicate promising accuracy in disease classification and lesion segmentation. The research highlights the artificial intelligence's possibilities as a clinical radiology assistive tool, emphasizing enhanced diagnostic precision, reduced reporting time, and greater accessibility to healthcare.