AI in Imaging for Diagnosis

Abstract

Medical imaging plays a critical role in diagnosing and monitoring various health conditions by offering insights into internal structures and abnormalities. However, traditional imaging techniques face limitations, such as dependency on manual interpretation, time consumption, and susceptibility to errors. Artificial Intelligence (AI) has emerged as a transformative tool, addressing these challenges with its ability to process vast datasets, enhance image quality, and deliver precise diagnostics. This chapter explores the principles of medical imaging, the integration of AI techniques like Convolutional Neural Networks (CNNs), and key image processing methods such as segmentation, classification, and object detection. Real-world applications in radiology, pathology, cardiology, neurology, and ophthalmology are discussed alongside case studies in breast cancer, lung cancer, and diabetic retinopathy. Finally, future directions, including personalized medicine and real-time diagnostics, are highlighted, emphasizing the pivotal role of AI in shaping the future of medical imaging.

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