Over the past few decades, biomedical imaging has witnessed significant technological advancements, ranging from Roentgen’s discovery of X-rays to modern tools such as computed tomography (CT), magnetic resonance imaging (MRI), and digital histopathology. These advancements have generated vast imagery data that offer valuable insights into patient health, disease progression, and cellular molecular structures. However, the sheer volume and diversity of this imaging data often exceed the capabilities of traditional analysis methods, presenting challenges in identifying meaningful patterns and correlations. Artificial intelligence (AI) has emerged as a powerful tool to efficiently and accurately analyze this data, revolutionizing the field of biomedical sciences. This chapter explores the transformative impact of AI on biomedical imaging, which facilitates the examination of intricate structures and dynamic processes within the human body. It explores the potential of AI for analyzing biomedical imagery data and addresses the challenges encountered during implementation. These challenges include managing gigapixel images, handling volumetric data, and dealing with temporal dimensions. To overcome these obstacles, this chapter discusses state-of-the-art approaches such as self-attention mechanism, multi-instance learning, semi-supervised learning, transfer learning, and sequence models to provide solutions for biomedical image analysis. With the continued integration of AI into biomedical imaging, this chapter serves as a comprehensive guide for leveraging AI to unlock deeper insights and enhance patient care in biomedical sciences.