Revolutionizing Healthcare

Advances in medical technologies and clinical practice book series, Journal Year: 2025, Volume and Issue: unknown, P. 347 - 366

Published: Jan. 31, 2025

The integration of computer vision and IoT is transforming healthcare by enabling precise diagnostics, real-time monitoring, personalized care. This chapter explores the synergy these technologies, highlighting their role in disease detection, smart medical devices, remote patient management. It addresses challenges such as data privacy, interoperability, ethical concerns, while showcasing real-world applications future directions. By leveraging AI-driven innovations, convergence holds immense potential to revolutionize healthcare, driving efficiency, accessibility, improved outcomes globally.

Language: Английский

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AI-Driven Advances in Low-Dose Imaging and Enhancement—A Review

Diagnostics, Journal Year: 2025, Volume and Issue: 15(6), P. 689 - 689

Published: March 11, 2025

The widespread use of medical imaging techniques such as X-rays and computed tomography (CT) has raised significant concerns regarding ionizing radiation exposure, particularly among vulnerable populations requiring frequent imaging. Achieving a balance between high-quality diagnostic minimizing exposure remains fundamental challenge in radiology. Artificial intelligence (AI) emerged transformative solution, enabling low-dose protocols that enhance image quality while significantly reducing doses. This review explores the role AI-assisted imaging, CT, X-ray, magnetic resonance (MRI), highlighting advancements deep learning models, convolutional neural networks (CNNs), other AI-based approaches. These technologies have demonstrated substantial improvements noise reduction, artifact removal, real-time optimization parameters, thereby enhancing accuracy mitigating risks. Additionally, AI contributed to improved radiology workflow efficiency cost reduction by need for repeat scans. also discusses emerging directions AI-driven including hybrid systems integrate post-processing with data acquisition, personalized tailored patient characteristics, expansion applications fluoroscopy positron emission (PET). However, challenges model generalizability, regulatory constraints, ethical considerations, computational requirements must be addressed facilitate broader clinical adoption. potential revolutionize safety, optimizing quality, improving healthcare efficiency, paving way more advanced sustainable future

Language: Английский

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FPGA Hardware Acceleration of AI Models for Real-Time Breast Cancer Classification

AI, Journal Year: 2025, Volume and Issue: 6(4), P. 76 - 76

Published: April 11, 2025

Breast cancer detection is a critical task in healthcare, requiring fast, accurate, and efficient diagnostic tools. However, the high computational demands latency of deep learning models medical imaging present significant challenges, especially resource-constrained environments. This paper addresses these challenges by presenting an FPGA hardware accelerator tailored for breast classification, leveraging Zynq XC7Z020 SoC. The system integrates FPGA-accelerated layers with ARM Cortex-A9 processor to optimize both performance resource efficiency. We developed modular IP cores, including Conv2D, Average Pooling, ReLU, using Vivado HLS maximize utilization. By adopting 8-bit fixed-point arithmetic, design achieves 15.8% reduction execution time compared traditional CPU-based implementations while maintaining classification accuracy. Additionally, our optimized approach significantly enhances energy efficiency, reducing power consumption from 3.8 W 1.4 63.15% reduction. improvement makes highly suitable real-time, power-sensitive applications, particularly embedded edge computing Furthermore, it underscores scalability efficiency FPGA-based AI solutions healthcare diagnostics, enabling faster more energy-efficient inference on devices.

Language: Английский

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Artificial intelligence in imaging for liver disease diagnosis

Frontiers in Medicine, Journal Year: 2025, Volume and Issue: 12

Published: April 25, 2025

Liver diseases, including hepatitis, non-alcoholic fatty liver disease (NAFLD), cirrhosis, and hepatocellular carcinoma (HCC), remain a major global health concern, with early accurate diagnosis being essential for effective management. Imaging modalities such as ultrasound (US), computed tomography (CT), magnetic resonance imaging (MRI) play crucial role in non-invasive diagnosis, but their sensitivity diagnostic accuracy can be limited. Recent advancements artificial intelligence (AI) have improved imaging-based assessment by enhancing pattern recognition, automating fibrosis steatosis quantification, aiding HCC detection. AI-driven techniques shown promise staging through US, CT, MRI, elastography, reducing the reliance on invasive biopsy. For steatosis, AI-assisted methods grading consistency, while detection characterization, AI models enhanced lesion identification, classification, risk stratification across modalities. The growing integration of into is reshaping workflows has potential to improve accuracy, efficiency, clinical decision-making. This review provides an overview applications imaging, focusing utility implications future diagnosis.

Language: Английский

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