Artificial Intelligence (AI) Hardware for Medical and Healthcare Applications
Edge processing refers to the technique of processing data at the location it is generated, rather than transmitting it to a centralized data processing system. This approach is particularly beneficial in healthcare application, where it enables faster and more efficient handling of medical data. By utilizing edge processing, healthcare systems can benefit from real-time data analysis, crucial for timely clinical decision-making. Furthermore, it ensures enhanced privacy and security for sensitive medical information, supports the development of wearable and portable devices for continuous health monitoring, and reduces the need for extensive bandwidth, thereby cutting costs and improving operational efficiency.
Incorporating AI algorithms into edge processing further boosts the signal processing capabilities of edge devices. These algorithms enhance accuracy in critical applications such as medical imaging, improving the resolution and yielding clearer and more detailed imaging data. AI also plays a pivotal role in Brain-Computer Interfaces (BCI), where precise signal interpretation is essential. This advanced integration is beneficial in elevating the medical imaging quality, diagnostics accuracy and effective treatment plans, marking a substantial advancement in medical technology and patient healthcare. Contemporary research is increasingly adopting AI-based hardware for edge processing in healthcare technology, significantly enhancing system speed and accuracy.
Our objective is to implement AI-based hardware to miniaturize and localize imaging processing units within neuroimaging systems. This approach aims to enhance processing speed and accuracy, and to achieve full wearability of these systems. The envisioned intelligent processing platform is not limited to imaging processing; it will also find applications in Brain-Computer Interfaces, Human-Robot Interaction, and neurorehabilitation. This innovation represents a significant step towards more efficient and user-friendly technologies in neuroscience and rehabilitation fields.
