Medical image analysis plays a crucial role in modern healthcare, enabling clinicians to visualize and interpret complex medical data for diagnosis, treatment planning, and image-guided surgery. However, this field faces numerous challenges, including image noise, artifacts, variability in imaging modalities, and the need for accurate and efficient analysis methods. This paper explores the current challenges and recent innovations in medical image analysis, focusing on advancements in machine learning, deep learning, and computer vision techniques. We discuss the impact of these innovations on improving diagnostic accuracy, treatment planning, and surgical outcomes. Additionally, we highlight future directions and potential advancements in medical image analysis to address remaining challenges and improve patient care.