Deep Learning Methods and Emerging Trends in Healthcare Diagnostics

Abstract

Deep learning has transformed the process of healthcare diagnostics by allowing the analysis of complicated medical data, such as imaging and clinical records, as well as genomic information, automatically. This review gives an overall summary of some important deep learning techniques, including convolutional, recurrent, transformers, and graph-based networks, and ways they can be used in radiology, pathology, cardiology, and personalized medicine. Emerging trends, such as explainable AI, federated learning, multimodal integration, and real-time diagnostics are addressed, as well as issues involving the quality of data, interpretability, ethical issues, and clinical implementation. Lastly, future research directions reflect the opportunity to have more customized, predictive, and collaborative diagnostic systems involving people and artificial intelligence, where deep learning can transform the future in terms of accuracy, efficiency, and patient outcomes.