MEDICAL IMAGING SOFTWARE USING DEEP LEARNING FOR AUTOMATED DIAGNOSIS AND RADIOLOGY WORKFLOW OPTIMIZATION

Abstract

Medical imaging software using deep learning supports automated diagnosis and improves radiology workflow efficiency. The system analyzes X-rays, CT scans, MRI images, and ultrasound data to identify abnormalities such as tumors, fractures, infections, and organ damage. Deep learning models can highlight suspicious regions, classify findings, and prioritize urgent cases for radiologist review. Automated image segmentation, report preparation, and comparison with previous scans can reduce workload and reporting delays. Integration with hospital information systems enables secure image access and coordinated clinical decision making. Human review, model validation, data encryption, and audit controls remain essential for maintaining accuracy and patient safety. Overall, this software can support faster diagnosis, improve reporting consistency, and help radiologists manage increasing imaging volumes more effectively.