Case Studies and Clinical Applications of AI in Imaging

Abstract

AI is revolutionizing medical imaging by enhancing diagnostic accuracy, efficiency, and personalized care. This chapter explores AI's clinical applications through case studies in radiology, neurology, cardiology, and ophthalmology, showcasing its role in early disease detection, risk stratification, and treatment optimization. Key applications include deep learning for breast cancer screening, AI-assisted stroke detection, and tumor segmentation. Challenges such as data quality, model interpretability, bias, ethics, and regulatory compliance are addressed. Emerging trends like federated learning, synthetic data, and explainable AI are highlighted. The chapter underscores the need for interdisciplinary collaboration, research, and standardization to maximize AI's impact on medical imaging and patient care.

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