AI and the next medical revolution: deep learning's uncharted healthcare promise

Abstract Deep learning has shown tremendous potential for transforming healthcare by enabling more accurate diagnoses, improved treatment planning and better patient outcome predictions. In this comprehensive survey, we provide a detailed overview of the state-of-the-art deep learning techniques and their applications across the healthcare ecosystem. We first introduce the fundamentals of deep learning and discuss its key advantages compared to traditional machine learning approaches. We then present an in-depth review of major applications of deep learning in medical imaging, electronic health record analysis, genomics, medical robotics and other domains. For each application, we summarize key advancements, outline the technical details of state-of-the-art methods, discuss challenges and limitations and highlight promising directions for future work. We examine cross-cutting challenges in deploying deep learning in clinical settings, including interpretability, bias and data scarcity. We conclude by proposing a roadmap to accelerate the translation and adoption of high-impact healthcare applications of deep learning. Overall, this survey provides a comprehensive reference for researchers and practitioners working at the intersection of deep learning and healthcare.