AI-Driven Diagnostic Tools for Radiology: Exploring the Integration of VLSI Architectures to Enhance the Accuracy of Diagnostic Imaging Techniques

AI-powered diagnostic tools in radiology, when integrated with Very-Large-Scale Integration (VLSI) architectures, are revolutionizing medical imaging by significantly enhancing diagnostic accuracy and operational efficiency. This paper presents a novel AI-VLSI integrated system designed to accelerate and improve the accuracy of medical image processing, specifically for detecting abnormalities such as tumors. The system achieves a diagnostic accuracy of $96.2 \%$, processes images in real-time at 25 frames per second (FPS), and optimizes energy consumption at 1.8 MB/Watt. These results demonstrate the system’s effectiveness in supporting radiologists by providing fast and reliable diagnostic insights. However, challenges remain in further optimizing the system for diverse imaging modalities and clinical scenarios. Future work will focus on refining the integration of AI algorithms, improving hardware scalability, and exploring next- generation VLSI technologies to enhance the system’s robustness and broaden its applicability. This research underscores the potential of AI and VLSI architectures to transform diagnostic radiology, paving the way for more efficient and accurate medical imaging solutions.