This chapter provides an overview of deep learning in medical image processing, discussing its state-of-the-art applications and future directions. Deep learning has emerged as a key method for improving the accuracy and efficiency of medical image analysis, particularly in tasks such as segmentation, classification, and disease detection. The chapter highlights the challenges and open research issues in applying deep learning to medical imaging, including the availability of large annotated datasets, privacy concerns, data interoperability, and the black-box nature of deep learning models. Despite these challenges, deep learning is expected to revolutionize various fields within healthcare, such as ophthalmology, pathology, cancer detection, and personalized medicine. The chapter also reviews specific applications of deep learning in diabetic retinopathy, histological and microscopic element detection, gastrointestinal diseases, cardiac imaging, tumor detection, and Alzheimer's and Parkinson's disease detection. Finally, it discusses the need for extensive inter-organization collaboration, the importance of capitalizing on big image data, advancements in deep learning methods, and the acceptance of deep learning by healthcare professionals.This chapter provides an overview of deep learning in medical image processing, discussing its state-of-the-art applications and future directions. Deep learning has emerged as a key method for improving the accuracy and efficiency of medical image analysis, particularly in tasks such as segmentation, classification, and disease detection. The chapter highlights the challenges and open research issues in applying deep learning to medical imaging, including the availability of large annotated datasets, privacy concerns, data interoperability, and the black-box nature of deep learning models. Despite these challenges, deep learning is expected to revolutionize various fields within healthcare, such as ophthalmology, pathology, cancer detection, and personalized medicine. The chapter also reviews specific applications of deep learning in diabetic retinopathy, histological and microscopic element detection, gastrointestinal diseases, cardiac imaging, tumor detection, and Alzheimer's and Parkinson's disease detection. Finally, it discusses the need for extensive inter-organization collaboration, the importance of capitalizing on big image data, advancements in deep learning methods, and the acceptance of deep learning by healthcare professionals.