The future of MedTech is robotic—hospitals will be fully automated, with AI-driven surgical systems, robotic assistants, and autonomous patient care transforming healthcare as we know it. Building AI-driven robotic systems poses several key challenges. Integrating data collection with expert insights is one. Creating detailed biomechanical simulations for realistic anatomy, sensors…
]]>The demand for real-time insights and autonomous decision-making is growing across industries, and healthcare and medical devices are no exception. Relying on real-time edge AI, the next generation of healthcare promises to deliver more precise treatments, improve patient outcomes, and increase operational efficiencies. Operating rooms of the future, for example…
]]>NVIDIA SDKs have been instrumental in accelerating AI applications across a spectrum of use cases spanning smart cities, medical, and robotics. However, achieving a production-grade AI solution that can deployed at the edge to support human and machine collaboration safely and securely needs both high-quality hardware and software tailored for enterprise needs. NVIDIA is again accelerating…
]]>Edge AI developers are building AI applications and products for safety-critical and regulated use cases. With NVIDIA Holoscan 1.0, these applications can incorporate real-time insights and processing in milliseconds. With the recent release of NVIDIA Holoscan 1.0, developers can more easily build production-ready applications for multimodal, real-time sensor processing.
]]>As large language models (LLMs) become more powerful and techniques for reducing their computational requirements mature, two compelling questions emerge. First, what is the most advanced LLM that can be run and deployed at the edge? And second, how can real-world applications leverage these advancements? Running a state-of-the-art open-source LLM like Llama 2 70B, even at reduced FP16…
]]>Demand for real-time insights and autonomous decision-making is growing in various industries. To meet this demand, we need scalable edge-solution platforms that can effectively process AI-enabled sensor data right at the source and scale out to on-premises or cloud compute resources. However, developers face many challenges in using AI and sensor processing at the edge: Before…
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