In the ever-evolving landscape of technology, digital imaging stands out for the changes it has step with both art and science of visual representation. The imaging has always been crucial in the processes of medical diagnosis and prognosis, and thus offers a great contribution towards SDG 3. Recently, several technologies including Machine Learning, Deep Learning, Convolutional Neural Networks, Generative Adversarial Networks, and Recurrent Neural Networks have reshaped the way conventional imaging was done. These techniques are used for image classification, segmentation, synthesis, enhancement, and generation of medical reports. The paradigm-changing effects of Transfer Learning and Deep Reinforcement Learning in medical image interpretation and treatment optimization are expounded upon. This chapter discusses the potential of data science in digital imaging: noise reduction, edge detection, predictive modeling, CAD systems, Natural Language Processing (NLP) for text and sentiment analysis. Furthermore, the chapter presents the future impacts made by technologies and further elaborates the challenges and ethical concerns, which include data privacy and standardization. It calls for responsible innovation, transparency, and stakeholder engagement to deploy the full potential of digital imaging toward the advancement of patient-centered care and scientific discovery in reshaping healthcare delivery in the future.