Dear colleagues,

The chapter represents dynamic evolution in imaging technologies and novel computational applications.Notably, medical imaging has seen significant attention, with a focus on Computer-Aided Systems (CAD).CAD's impact spreads to diverse medical disciplines like neuroscience, cardiology, oncology, and many other medical domains. For instance, ultrasound diagnostics successfully address conditions such as osteoarthritis, breast carcinoma and cardiovascular issues.

Computational imaging, a rapidly growing field, deploys mathematical models and algorithms to enhance image quality, extending its influence on varieties of vision systems. This area emphasizes the substantial progress in imaging technology, marked by continual computational applications and advancements, particularly in medical imaging. The integration of CAD and computational technologies positively transforms and enhances clinical diagnosis and proposes multiple applications beyond traditional boundaries (Hussain et al., 2022). In parallel, AI-powered radiology has revolutionized healthcare, progressing from X-ray discovery to modern medical image analysis through machine learning and deep learning. Modern radiology incorporates modalities like CT, MRI, PET, ultrasound, X-rays, etc., providing insights into anatomical, physiological, and molecular processes leading to the development of pathological conditions. The evolution from film-based to digital radiography and the development of Picture Archiving and Communication Systems (PACS) contribute to efficient image acquisition and sharing. Functional techniques like PET and SPECT illuminate metabolic and biological processes, while 3D imaging and hybrid technologies like PET/CT and SPECT/CT enhance diagnostic accuracy (Najjar, 2023).

Imaging technologies offering real-time visualization hold the potential for improved patient diagnosis and treatment. The future of radiology is an integration with virtual/augmented reality and AI, particularly machine learning. The last two decades have seen refined CAD tools based on ML, promising integrated diagnostic services that encompass radiology, pathology, and genomics data (Rundo et al., 2020). The merge of AI and technological progress holds a high potential for significant transformations in healthcare; nevertheless, ethical considerations demand meticulous attention. The existence of ethical concerns, especially those entailing bias and transparency in AI decision-making, requires a thorough examination and, possibly, government legislation.

Imaging technologies, CAD, computational imaging, AI-powered radiology, medical image analysis, machine learning