Artificial Intelligence in Diagnostic Medical Image Processing for Advanced Healthcare Applications

Recent advancements in biomedical imaging modalities, including magnetic resonance imaging (MRI), X-ray, ultrasound, computed tomography (CT), electrical impedance tomography (EIT), positron emission tomography (PET), and (optical) microscopic imaging, have significantly propelled healthcare by elucidating intricate details of human tissues and organs. Despite these achievements, challenges persist in terms of image acquisition, processing, big medical data management, operator-dependent variabilities, and image processing intricacies. In this context, artificial intelligence (AI) has emerged as a transformative solution, alleviating operator-dependent variabilities and augmenting diagnostic, treatment planning, and patient care capabilities. In this chapter, we present a detailed exploration of AI applications across various imaging modalities, tracing their historical evolution in medical imaging. We study the workflow of AI in tasks such as image reconstruction, analysis, interpretation, segmentation, and enhancement. Additionally, we explore its role in computer-aided diagnosis (CAD), treatment planning, patient group classification, and predicting and reducing radiotherapy doses. This chapter encompasses discussions on benefits, challenges, ethical considerations, and future directions in the field of AI in biomedical imaging. This chapter underscores the pivotal role of AI in enhancing the efficiency, objectivity, reliability, and accuracy of medical imaging services.