Intelligent Medical Imaging Systems for Rapid Real-Time Diagnosis: Deep Learning, Edge AI, Hardware Acceleration, and Healthcare Intelligence

The rapid evolution of Artificial Intelligence (AI) has transformed medical imaging into an intelligent and data-driven clinical decision-support ecosystem. The growing demand for accurate, low-latency, scalable, and interpretable diagnostics has accelerated the integration of deep learning, Edge AI, and hardware acceleration in healthcare systems. Recent developments in intelligent medical imaging systems for real-time clinical diagnosis are examined in this article. The systematic review was conducted using peer-reviewed literature retrieved from IEEE Xplore, PubMed, Scopus, Web of Science, and ScienceDirect, covering studies published from 2020 to April 2026. 4,912 articles were screened, with 52 high-quality studies included for final synthesis. The review focuses on convolutional neural networks, federated learning, explainable AI, transformer-based architectures, multimodal imaging, and hardware accelerators, including Application-Specific Integrated Circuits (ASICs), Field-Programmable Gate Arrays (FPGAs), Tensor Processing Units (TPUs), Graphics Processing Units (GPUs), and neuromorphic processors. Findings indicate substantial improvements in diagnostic accuracy, computational efficiency, scalability, and low-latency inference across cardiology, oncology, pathology, radiology, and ophthalmology. Main challenges include heterogeneous data sources, high energy demands, limited clinical validation, and lack of interpretability, privacy concerns, interoperability issues, and regulatory barriers. Emerging directions include digital twins, lightweight AI, multimodal foundation models, sustainable edge intelligence, and privacy-preserving federated ecosystems, highlighting the transformative potential of intelligent medical imaging in precision healthcare.