Advancements in Disease Detection and Volume Reduction: A Review on Medical Imaging and Healthcare Innovations

Abstract

Medical imaging remains a cornerstone of modern healthcare, essential for accurate disease detection and optimized treatment planning. This review examines advanced imaging technologies such as X-ray, CT, MRI, and ultrasound, alongside emerging methodologies incorporating machine learning (ML) and artificial intelligence (AI). Techniques for disease detection focus on identifying abnormalities, lesions, or pathological transformations, while strategies for volumetric reduction address minimizing affected tissues or organs. The integration of these approaches facilitates timely interventions and aids in evaluating treatment efficacy with precision. Despite significant advancements, challenges persist, including enhancing detection sensitivity, improving volumetric accuracy, and effectively integrating multi-modal imaging datasets. This discussion emphasizes current innovations, barriers to progress, and future directions, advocating for solutions that advance personalized healthcare. Furthermore, the role of mobile applications for efficient processing and analysis, combined with the scalability of cloud storage solutions, underscores the importance of leveraging technology to address contemporary medical imaging demands.