Industrial AI isn’t slowing down. Germany is ready. Following London Tech Week and GTC Paris at VivaTech, NVIDIA founder and CEO Jensen Huang’s European tour continued with a stop in Germany to discuss with Chancellor Friedrich Merz — pictured above — new partnerships poised to bring breakthrough innovations on the world’s first industrial AI cloud. This AI factory, to be located in Germany and operated by Deutsche Telekom, will enable Europe’s industrial leaders to accelerate manufacturing applications including design, engineering, simulation, digital twins and robotics. “In the era of AI, every manufacturer needs two factories: one for making things, and one for creating the intelligence that powers them,” said Jensen Huang, founder and CEO of NVIDIA. “By building Europe’s first industrial AI infrastructure, we’re enabling the region’s leading industrial companies to advance simulation-first, AI-driven manufacturing.” “Europe’s technological future needs a sprint, not a stroll,” said Timotheus Höttges, CEO of Deutsche Telekom AG. “We must seize the opportunities of artificial intelligence now, revolutionize our industry and secure a leading position in the global technology competition. Our economic success depends on quick decisions and collaborative innovations.” This AI infrastructure — Germany’s single largest AI deployment — is an important leap for the nation in establishing its own sovereign AI infrastructure and providing a launchpad to accelerate AI development and adoption across industries. In its first phase, it’ll feature 10,000 NVIDIA Blackwell GPUs — spanning NVIDIA DGX B200 systems and NVIDIA RTX PRO Servers — as well as NVIDIA networking and AI software. NEURA Robotics’ training center for cognitive robots. NEURA Robotics, a Germany-based global pioneer in physical AI and cognitive robotics, will use the computing resources to power its state-of-the-art training centers for cognitive robots — a tangible example of how physical AI can evolve through powerful, connected infrastructure. At this work’s core is the Neuraverse, a seamlessly networked robot ecosystem that allows robots to learn from each other across a wide range of industrial and domestic applications. This platform creates an app-store-like hub for robotic intelligence — for tasks like welding and ironing — enabling continuous development and deployment of robotic skills in real-world environments. “Physical AI is the electricity of the future — it will power every machine on the planet,” said David Reger, founder and CEO of NEURA Robotics. “Through this initiative, we’re helping build the sovereign infrastructure Europe needs to lead in intelligent robotics and stay in control of its future.” Critical to Germany’s competitiveness is AI technology development, including the expansion of data center capacity, according to a Deloitte study. This is strategically important because demand for data center capacity is expected to triple over the next five years to 5 gigawatts. Driving Germany’s Industrial Ecosystem Deutsche Telekom will operate the AI factory and provide AI cloud computing resources to Europe’s industrial ecosystem. Customers will be able to run NVIDIA CUDA-X libraries, as well as NVIDIA RTX- and Omniverse-accelerated workloads from leading software providers such as Siemens, Ansys, Cadence and Rescale. Many more stand to benefit. From the country’s robust small- and medium-sized businesses, known as the Mittelstand, to academia, research and major enterprises — the AI factory offers strategic technology leaps. A Speedboat Toward AI Gigafactories The industrial AI cloud will accelerate AI development and adoption from European manufacturers, driving simulation-first, AI-driven manufacturing practices and helping prepare for the country’s transition to AI gigafactories, the next step in Germany’s sovereign AI infrastructure journey. The AI gigafactory initiative is a 100,000 GPU-powered program backed by the European Union, Germany and partners. Poised to go online in 2027, it’ll provide state-of-the-art AI infrastructure that gives enterprises, startups, researchers and universities access to accelerated computing through the establishment and expansion of high-performance computing centers. As of March, there are about 900 Germany-based members of the NVIDIA Inception program for cutting-edge startups, all of which will be eligible to access the AI resources. NVIDIA offers learning courses through its Deep Learning Institute to promote education and certification in AI across the globe, and those resources are broadly available across Germany’s computing ecosystem to offer upskilling opportunities. Additional European telcos are building AI infrastructure for regional enterprises to build and deploy agentic AI applications. Learn more about the latest AI advancements by watching Huang’s GTC Paris keynote in replay.

Nvidia today announced a significant expansion of the Nvidia Omniverse Blueprint for AI factory digital twins, now available as a preview.Read More

As robots increasingly make their way to the largest enterprises’ manufacturing plants and warehouses, the need for access to critical business and operational data has never been more crucial. At its Sapphire conference, SAP announced it is collaborating with NEURA Robotics and NVIDIA to enable its SAP Joule agents to connect enterprise data and processes with NEURA’s advanced cognitive robots. The integration will enable robots to support tasks including adaptive manufacturing, autonomous replenishment, compliance monitoring and predictive maintenance. Using the Mega NVIDIA Omniverse Blueprint, SAP customers will be able to simulate and validate large robotic fleets in digital twins before deploying them in real-world facilities. Virtual Assistants Become Physical Helpers AI agents are traditionally confined to the digital world, which means they’re unable to take actionable steps for physical tasks and do real-world work in warehouses, factories and other industrial workplaces. SAP’s collaboration with NVIDIA and NEURA Robotics shows how enterprises will be able to use Joule to plan and simulate complex and dynamic scenarios that include physical AI and autonomous humanoid robots to address critical planning, safety and project requirements, streamline operations and embody business intelligence in the physical world. Revolutionizing Supply Chain Management: NVIDIA, SAP Tackle Complex Challenges Today’s supply chains have become more fragile and complex with ever-evolving constraints around consumer, economic, political and environmental dimensions. The partnership between NVIDIA and SAP aims to enhance supply chain planning capabilities by integrating NVIDIA cuOpt technology — for real-time route optimization — with SAP Integrated Business Planning (IBP) to enable customers to plan and simulate the most complex and dynamic scenarios for more rapid and confident decision making. By extending SAP IBP with NVIDIA’s highly scalable, GPU-powered platform, customers can accelerate time-to-value by facilitating the management of larger and more intricate models without sacrificing runtime. This groundbreaking collaboration will empower businesses to address unique planning requirements, streamline operations and drive better business outcomes. Supporting Supply Chains With AI and Humanoid Robots SAP Chief Technology Officer Philipp Herzig offered a preview of the technology integration in a keynote demonstration at SAP Sapphire in Orlando, showing the transformative potential for physical AI in businesses in how Joule — SAP’s generative AI co-pilot — works with data from the real world in combination with humanoid robots. “Robots and autonomous agents are at the heart of the next wave of industrial AI, seamlessly connecting people, data, and processes to unlock new levels of efficiency and innovation,” said Herzig. “SAP, NVIDIA and NEURA Robotics share a vision for uniting AI and robotics to improve safety, efficiency and productivity across industries.” With the integration of the Mega NVIDIA Omniverse Blueprint, enterprises can harness physical AI and digital twins to enable new AI agents that can deliver real-time, contextual insights and automate routine tasks. The collaboration between the three companies allows NEURA robots to see, learn and adapt in real time — whether restocking shelves, inspecting infrastructure or resolving supply chain disruptions. SAP Joule AI Agents are deployed directly onto NEURA’s cognitive robots, enabling real-time decision-making and the execution of physical tasks such as inventory audits or equipment repairs. The robots learn continuously in physically accurate digital twins, powered by NVIDIA Omniverse libraries and technologies,  and using business-critical data from SAP applications, while the Mega NVIDIA Omniverse Blueprint helps evaluate and validate deployment options and large-scale task interactions. Showcasing Joule’s Data-Driven Insights on NEURA Robots The technology demonstration showcases the abilities of NEURA robots to handle tasks guided by Joule’s data-driven insights. Businesses bridging simulation-to-reality deployments can use the integrated technology stack to provide zero-shot navigation and inference to address defect rates and production line inefficiencies without interrupting operations. Herzig showed how Joule can direct a NEURA 4NE1 robot to inspect a machine in the showroom, powered by AI agents, scanning equipment and triggering an SAP Asset Performance management alert before a failure disrupts operations. It’s but a glimpse into the future of what’s possible with AI agents and robotics.

NVIDIA and Taiwan’s manufacturing ecosystem, including Delta Electronics, Foxconn, TSMC and Wistron, are showcasing this week at COMPUTEX in Taipei the crucial role digital twins play in accelerating industrial AI. These electronics, semiconductor and robotics manufacturing leaders are using Universal Scene Description (OpenUSD) and NVIDIA Omniverse libraries and blueprints to develop physically based digital twins. This is transforming factory planning by unlocking new operational efficiencies and accelerating the development, testing and validation of autonomous robots and robotic fleets. Many of these manufacturers are also extending the digitalization of their factories to the real world, using the NVIDIA AI Blueprint for video search and summarization (VSS) — now generally available and part of the NVIDIA Metropolis platform — to deploy video analytics AI agents into their operations and drive additional automation and optimizations in defect detection and other operations. Taiwan Manufacturers Optimize Planning and Operations With Simulation and AI Agents  Taiwan’s leading electronics and semiconductor manufacturers are using digital twins, physically based simulation and AI agents to optimize existing operations and vastly accelerate the planning and commissioning of new factories. Foxconn is leading the way. At its Taiwan facilities, Foxconn engineers rely on the Fii Digital Twin platform, developed with OpenUSD, Siemens and Omniverse technologies, to design and simulate robot work cells, assembly lines and entire factory layouts. These digital twins connect to material control systems and use Autodesk Flexsim, NVIDIA cuOpt and NVIDIA Isaac Sim to enable engineers to simulate and dynamically optimize the flow of materials, equipment, autonomous mobile robots (AMRs), automated guided vehicles, and other robots and humans. By developing a standard digital twin model for their factories, Foxconn can quickly migrate and easily reconfigure its designs and plans for new factory deployments. Foxconn is using the NVIDIA Isaac GR00T N1 model, the NVIDIA Isaac GR00T-Mimic blueprint for synthetic manipulation motion generation and NVIDIA Isaac Lab to train industrial manipulator arms and humanoid robots for performing complex tasks such as screw-tightening, pick and place, assembly and cable insertion. Foxconn robotics developers use the Mega NVIDIA Omniverse Blueprint to simulate and test large robotic fleets comprising AMRs, manipulators and humanoid robots before deploying them in facilities. To accelerate analysis and decision-making, Foxconn engineers use their digital twin platform to conduct thermal assessments of POD rooms across different scenarios. By connecting their digital twins to the Cadence Reality Digital Twin Platform and integrating NVIDIA PhysicsNeMo frameworks, teams can conduct thermal simulations 150x faster, reduce thermal risks and identify energy-saving opportunities. Using the Omniverse Blueprint for AI factory digital twins, Foxconn can simulate and test GB200 Grace Blackwell Superchips in liquid-cooled PODs to replicate the conditions of an AI factory. Credit: Foxconn The company is also deploying video analytics AI agents using the VSS blueprint from NVIDIA Metropolis for real-time video analysis and insights in live production scenarios. TSMC is collaborating with an AI-powered digital twins startup to optimize the planning and construction of its new fabs. TSMC taps into an AI engine and applications built with Omniverse libraries to transform traditional 2D computer-aided designs into rich, interactive 3D layouts of their complex facilities, including specialized areas like clean rooms. Credit: TSMC Visualizing these optimized layouts in a digital twin allows planning teams to proactively identify and resolve equipment collisions, understand system interdependencies, and assess impacts on space and operational key performance indicators. This AI-driven approach is enhanced by NVIDIA cuOpt for optimization and reinforcement learning with NVIDIA Isaac Lab, enabling the generation of intricate, multilevel piping systems in seconds — a task that traditionally requires substantial time and effort. This enables engineers to virtually validate complex pipe routing and drastically reduce design revisions, ultimately streamlining the entire fab development process. TSMC also uses vision language models and vision foundation models to improve automated defect classification workflows — boosting efficiency to classify wafer product defects for engineers to pinpoint potential root causes for the issues. Beyond the use of digital twins and vision AI, TSMC also taps into NVIDIA CUDA-X software libraries and NVIDIA GPUs to accelerate its entire semiconductor chip design workflow — from lithography with NVIDIA cuLitho to semiconductor process simulation. Wistron teams drive operational efficiencies, optimize layout planning of their plants, and train robots and workers with the Wistron Digital Twin (WiDT) platform. The platform is powered by software from Autodesk, Cadence and Microsoft and taps into NVIDIA AI and Omniverse libraries. By connecting the WiDT platform to generative AI tools and real-time data from surface mount technology machines and shopfloor control systems, operations teams can visualize real-time dashboards to quickly diagnose and improve machine and plant performance. Wistron robotics developers use the platform, and its integration with NVIDIA Isaac Sim, to simulate and test robotic arms. With a simulation-first approach, teams reduced the time needed for each arm to assemble parts on the production line by 12 seconds. Credit: Wistron The Wistron digital twin platform also uses the VSS blueprint to create and curate training videos for teaching workers how to perform and manage complex tasks and scenarios. The platform uses NVIDIA Cosmos Tokenizer to help teams analyze and break down worker actions on the production line and improve standard operating procedures. This approach is enabling Wistron to accelerate onboarding, improve worker productivity and ensure safety. Wiwynn uses AI-enabled digital twins built with Omniverse technologies to optimize factory layouts, simulate production, integrate cobots and enhance quality control through improved inspection and analysis. These solutions have driven significant manufacturing and logistics innovation and efficiencies. Pegatron’s PEGAVERSE and PEGAAi platforms equip engineers and factory managers with digital twins that support many use cases, including factory planning, predictive maintenance, process optimization, resource planning, remote monitoring and quality control. Teams also use the platforms to build visual AI agents to help workers perfect complex assembly tasks. These AI agents, developed with the NVIDIA AI Blueprint for VSS and NVIDIA Metropolis, have enabled Pegatron to augment assembly processes, reduce labor costs by 7% and decrease assembly line defect rates by 67%. Kenmec and MetAI are using Omniverse technologies and the Mega NVIDIA Omniverse Blueprint to build physically accurate digital twins for simulating, testing and deploying warehouse automation solutions. Together, the teams virtualized the entire Chief Smart Logistics Center, creating a full-fidelity simulation environment that brings together physical dynamics, real-time controller logic, AI-driven testing and optimization — all within a simulated environment. GIGABYTE operations teams are using digital twins developed with Omniverse libraries and connected to live IoT data from the manufacturing floor to improve operational monitoring of production systems. By visually flagging anomalies, including equipment issues and delays, the digital twins help teams quickly identify issues, conduct root cause analysis and take corrective actions. Quanta Cloud Technology engineering, operations and logistics teams collaborate using digital twin solutions built with Omniverse to accelerate factory planning. Digital twins provide these cross-functional teams with access to the latest design data, enabling them to provide immediate feedback on proposed layouts, which leads to optimized workflows and improved space utilization. Teams can further extend collaboration sessions to external customers and suppliers so they can remotely contribute to design reviews and validation. Credit: Quanta Manufacturers Embrace Digital Twins to Accelerate Robotics Development In addition to creating the future in manufacturing, Taiwan manufacturers are using digital twins, powered by Omniverse libraries and blueprints, to develop the next wave of AI-enabled robots. Delta Electronics is using Isaac Sim to optimize electronic component production and to simulate, train and validate its entire range of industrial robots — from AMRs to industrial manipulators. Credit: Delta Electronics The company is transforming its expertise into a service by designing a cyber-physical integrated classroom to be launched soon in Taiwan, where customers learn to use the DIATwin platform to simulate and integrate Delta’s industrial equipment and robots to ensure a more effective implementation into their own production lines. Credit: Techman Robot Techman Robot is advancing intelligent automation at Volkswagen’s Transparent Factory. Using Isaac Sim, Techman’s AI Cobots learn to operate on GESSbot AMRs in physically accurate simulations to perform real-time assembly, inspection and adaptive manipulation tasks with precision. By simulating robot behavior and workflows virtually, Techman Robot has reduced the time to program robots by 70% and improved robot productivity by 20%. Credit: Foxlink Foxlink is using the Isaac GR00T N1 model to add generalized intelligence and autonomy to its industrial robots used in manufacturing facilities. Solomon’s AI vision solution, powered by NVIDIA Isaac Manipulator CUDA-X acceleration libraries, is helping Inventec significantly accelerate its robotic server inspection process by boosting complex motion planning speed by up to 8x and reducing errors by 50%. Kudan is integrating its Visual SLAM technology with Isaac Perceptor CUDA-X acceleration libraries into NexAIoT’s AMR, NexMOV-2. This integration uses advanced 3D perception and navigation, enabling them to navigate complex, unstructured environments such as manufacturing, logistics and healthcare facilities with greater precision and reliability. MSI is powering its industrial robots with the NVIDIA Jetson AGX Orin module to perform a variety of tasks, from pick-and-place and material handling to delivering payloads inside large warehouses and facilities. Credit: Adata In healthcare, Adata and Advantech are jointly using Isaac Sim, Isaac Perceptor and Jetson Orin to develop AMRs for disinfecting hospitals. This collaboration has reduced deployment time by 70% and made the disinfection process 3x faster. Ubitus is also using the Isaac platform to train G1 humanoid robots to deliver medical checkup materials and specimens, helping alleviate labor shortages in hospitals. Learn more by watching the COMPUTEX keynote from NVIDIA founder and CEO Jensen Huang and attending sessions at NVIDIA GTC Taipei, running through May 22. See notice regarding software product information. Featured image courtesy of Quanta (top left), Wistron (top right), Foxconn (bottom left), Pegatron (bottom right).

Empowering engineering teams with more tools for building AI factories, NVIDIA today announced a significant expansion of the NVIDIA Omniverse Blueprint for AI factory digital twins, now available as a preview. The blueprint features new integrations across the AI factory power, cooling and networking ecosystems with industry leaders Delta Electronics, Jacobs and Siemens, joining existing partners Cadence, Schneider Electric with ETAP and Vertiv. This growing ecosystem unifies the design and simulation of billions of components required to build digital twins of AI factories. The expanded blueprint will equip engineering teams to design, simulate and optimize entire AI factories in physically accurate virtual environments, enabling early issue detection and the development of smarter, more reliable facilities. Built on reference architectures for NVIDIA GB200 NVL72-powered AI factories, the blueprint taps into Universal Scene Description (OpenUSD) asset libraries. This allows developers to aggregate detailed 3D and simulation data representing all aspects of the data center into a single, unified model, enabling them to design and simulate advanced AI infrastructure optimized for efficiency, throughput and resiliency. The Omniverse Blueprint for AI factory digital twins unifies AI factory power, cooling and networking components in one simulation. AI Factory Ecosystem Teams With NVIDIA The Omniverse Blueprint for AI factory digital twins brings together diverse partners and tools to optimize the design, simulation, deployment and operations of AI factories. Today, NVIDIA announced that new partners are contributing to the framework. Siemens is building 3D models according to the blueprint and engaging with the simulation-ready, or SimReady, standardization effort, while Delta Electronics is adding models of its equipment. Because these are built with OpenUSD, users get accurate simulations of their facility equipment. Jacobs is helping test and optimize the end-to-end blueprint workflow. They join leaders in data center power and cooling solutions like Schneider Electric with ETAP and Vertiv, which contribute SimReady assets to populate the digital twin of the AI factory with 3D models of power, cooling and mechanical systems. “As AI factories continue to scale at an unprecedented pace, the energy demands they generate are reshaping the entire digital infrastructure landscape,” says Tanuj Khandelwal, CEO of ETAP. “Using the Omniverse Blueprint and SimReady assets, customers can test and optimize energy efficiency for the complexity and intensity of their AI workloads before even breaking ground.” Connections to the Cadence Reality Digital Twin Platform and ETAP provide thermal and power simulation, enabling engineering teams to test and optimize power, cooling and networking long before construction begins. These contributions help NVIDIA and its partners reshape how AI infrastructure is built to achieve smarter designs, avoid downtime and get the most out of AI factories. “Digital twins are fundamental to meet the escalating global demand for AI factories,” said Ben Gu, corporate vice president of R&D for multiphysics system analysis at Cadence. “The integration of the Cadence Reality Digital Twin Platform with the NVIDIA Omniverse Blueprint transforms the entire engineering process to design AI factories more efficiently and operate them more effectively than ever before. We are excited to continue our full-stack collaboration with NVIDIA.” Building SimReady Assets for AI Factories The OpenUSD-based models within the blueprint are inherently SimReady, designed from the ground up to be physics-based. This is especially valuable for developing and testing physical AI and agentic AI within these AI factories, enabling rapid and large-scale industrial AI simulations of power and cooling systems, building automation and overall IT operations. SimReady standardization workflow enables developers to view standardized simulations of thermal airflow within a digital twin environment. A key enhancement to this blueprint is the SimReady standardization workflow. Originally developed as a SimReady standardization proposal to streamline NVIDIA’s internal creation of OpenUSD assets, this now publicly available, industry-agnostic resource offers standardized requirements and processes for developing SimReady capabilities. It empowers data center developers and owners to efficiently establish, optimize and rigorously test their own digital twins of critical infrastructure, particularly for electrical and thermal management within AI factories. A Smarter Road to AI Infrastructure The expansion of the NVIDIA Omniverse Blueprint for AI factory digital twins marks a significant leap forward in how engineers design, simulate and build the sophisticated infrastructure required for industrial AI. By providing a unified and physically accurate digital twin, built on the robust foundation of OpenUSD and guided by SimReady standardization, this blueprint enables the industry to de-risk development, optimize performance and accelerate the deployment of next-generation AI factories. Learn more about NVIDIA Omniverse and preview the Omniverse Blueprint for AI factory digital twins. Watch the COMPUTEX keynote from NVIDIA founder and CEO Jensen Huang, as well as NVIDIA GTC Taipei 2025 sessions. See notice regarding software product information. Featured image courtesy of Cadence, ETAP, Schneider Electric, and Vertiv.

Leading healthcare organizations across the globe are using agentic AI, robotics and digital twins of medical environments to enhance surgical precision, boost workflow efficiency, improve medical diagnoses and more. Physical AI and humanoid robots in hospitals have the potential to automate routine tasks, assist with patient care and address workforce shortages. This is especially crucial in places where challenges to optimal healthcare services are paramount. Such challenges include hospital overcrowding, an aging population, rising healthcare costs and a shortage of medical professionals, all of which are affecting Taiwan, as well as many other regions and countries. At the COMPUTEX trade show in Taipei, NVIDIA today showcased how leading Taiwan medical centers are collaborating with top system builders to integrate smart hospital technologies and other AI-powered healthcare solutions that can help reduce these issues and save millions of lives. Cathay General Hospital, Chang Gung Memorial Hospital (CGMH), National Taiwan University Hospital (NTUH) and Taichung Veterans General Hospital (TCVGH) are among the top centers in the region pioneering healthcare AI innovation. Deployed in collaboration with leading system builders such as Advantech, Onyx, Foxconn and YUAN, these solutions tap into NVIDIA’s agentic AI and robotics technologies, including the NVIDIA Holoscan and IGX platforms, NVIDIA Jetson for embedded computing and NVIDIA Omniverse for simulating virtual worlds with OpenUSD. CGMH Boosts AI-Powered Medical Imaging With an average of 8.2 million outpatient visits and 2.4 million hospitalizations a year,  CGMH estimates that a third of the Taiwanese population has sought treatment at its vast network of hospitals in Taipei and seven other cities. The organization is pioneering smart hospital innovation by enhancing surgical precision and workflow efficiency through advanced, AI-powered colonoscopy workflow solutions, developed in collaboration with Advantech and based on the NVIDIA Holoscan platform, which includes the Holoscan SDK and the Holoscan Sensor Bridge running on NVIDIA IGX. NVIDIA Holoscan is a real-time sensor processing platform for edge AI compute, while NVIDIA IGX offers enterprise-ready, industrial edge AI purpose-built for medical environments. Using these platforms, CGMH is accelerating AI integration in its colonoscopy diagnostics procedures. Deployed in gastrointestinal consultation rooms, the AI-powered tool collects colonoscopy streams to train a customized model built on Holoscan and provides real-time colonic polyps identification and classification. Colonoscopy tools at CGMH. Image courtesy of CGMH. CGMH’s AI infrastructure — comprising NVIDIA accelerated computing, NVIDIA DGX systems, the MONAI framework, NVIDIA TensorRT-LLM open-source library, NVIDIA Dynamo inference framework, and the NVIDIA NeMo and Clara platforms — enables accelerated research and development across the organization. CGMH serves nearly 50 AI agent models that daily help the hospital analyze medical imaging, improving diagnostic accuracy, throughput and real-time inference at scale. For example, NVIDIA Triton-powered AI sped newborn examination record processing by 10x. Cathay General Hospital Improves Diagnostics With AI Cathay General Hospital, a Taipei-based healthcare center that provides hospital management and medical services, has collaborated with National Taiwan University Hospital, medical computer manufacturer Onyx and software provider aetherAI to develop an AI-assisted colonoscopy system that highlights lesions, detects hard-to-spot polyps and issues alerts to help physicians with diagnoses. Polyp detection during colonoscopy. Image courtesy of aetherAI and Onyx. Powered by a compact, plug-and-play AI BOX device — built with the NVIDIA Jetson AGX Xavier module — the AI system is trained on over 400,000 high-quality, physician-annotated images collected from patients with diverse and severe lesions over four years. The system can achieve up to 95.8% accuracy and sensitivity, and studies have shown that it can improve adenoma detection rates by up to 30%. These enhancements assist physicians in reducing diagnostic errors and making more informed treatment decisions, ultimately contributing to improved patient outcomes. NTUH Detects Liver Tumors, Cardiovascular Risks With AI In the 100+ years since its founding, NTUH has nurtured countless professionals in medicine and is renowned for its trusted clinical care. The national teaching hospital is now adopting AI imaging to more quickly, accurately diagnose patients. NTUH’s HeaortaNet model, trained on more than 70,000 axial images from 200 patients, automates CT scan segmentation of the heart, including the aorta and other arteries, in 3D, enabling rapid analysis of risks for cardiovascular disease. The model, which achieves high segmentation accuracy for the pericardium and aorta, significantly reduced data processing time per case from an hour to about 0.4 seconds. In addition, NTUH collaborated with the Good Liver Foundation and system builder YUAN to develop a diagnostic-assistance system for liver cancer detection during ultrasounds. It taps into an NVIDIA Jetson Orin NX module and a deep learning model trained on more than 5,000 annotated ultrasound images to identify malignant and benign liver tumors in real time. YUAN and NTUH’s liver cancer detection system turns an ultrasound device into an AI-assisted diagnostic tool. Image courtesy of YUAN. NVIDIA DeepStream and TensorRT SDKs accelerate the system’s deep learning model, ultimately helping clinicians detect tumors earlier and more reliably. In addition, NTUH is using NVIDIA DGX to train AI models for its system that detects pancreatic cancer from CT scans. TCVGH Streamlines Multimodal Imaging and Clinical Documentation Workflows With AI  Taichung Veterans General Hospital (TCVGH), a medical center and a teaching hospital administered by the Veterans Affairs Council in Taipei, has partnered with Foxconn to build physical and digital robots to augment staffing, improving clinician productivity and patient experiences. Foxconn developed an AI system that can analyze medical images and spot signs of breast cancer earlier than traditional methods, using NVIDIA Hopper GPUs, NVIDIA DGX systems and the MONAI framework. By tapping into clinical data and multimodal AI imaging, the system creates 3D virtual breast models, quickly highlighting areas of concern in scans to help radiologists make faster, more confident decisions. Foxconn is also working with TCVGH to build smart hospital solutions like the AI nursing collaborative robot Nurabot and tapping into NVIDIA Omniverse to create real-time digital twins of hospital environments, including nursing stations, patient wards and corridors. These digital replicas serve as high-fidelity simulations where Jetson-powered service robots can be trained to autonomously deliver medical supplies throughout the hospital, ultimately improving care efficiency. AI nursing collaborative robot Nurabot. Image courtesy of Foxconn. In addition, TCVGH has developed and deployed its Co-Healer system, which integrates the Taiwanese native large language model TAIDE-LX-7B to streamline clinical documentation processes with agentic AI. Co-Healer, built on the NVIDIA Jetson Xavier NX module, processes and helps summarize medical documents — such as nursing progress notes and health education materials — and supports medical exam preparation by providing students with instant access to nursing guidelines and patient-specific protocols for clinical procedures and diagnostic tests. This helps healthcare workers alleviate burnout while giving patients a clearer understanding of their diagnoses. Learn more about the latest AI advancements in healthcare at NVIDIA GTC Taipei, running May 21-22 at COMPUTEX.