Artificial Intelligence is an increasingly important component of modern healthcare. It enables advancements in AI-driven medical imaging solutions, patient monitoring, and robotic-assisted procedures. Medical AI applications continue to expand rapidly. Because of this, healthcare equipment manufacturers are shifting AI inference from cloud platforms to edge devices. This shift enables faster response times and lower latency. It also enhances data privacy and provides greater system reliability.
Medical systems like ultrasound, digital X-ray, and endoscopy require powerful computing platforms. These platforms must simultaneously process high-resolution images, execute AI inference, and drive multiple displays. Furthermore, long product lifecycle support and system stability are essential. Power efficiency and simplified maintenance are also critical design considerations for medical OEMs.
Medical equipment is becoming increasingly sophisticated. Developers must balance performance requirements, product longevity, and development efficiency. Some applications require design flexibility and processor scalability. For these, COM Express® modules provide a standardized computing platform. They separate processor technology from the application-specific carrier board design. This approach simplifies development and streamlines future platform upgrades.
Medical AI applications need a robust computer-on-module (COM). It must deliver heterogeneous computing performance and high-speed memory bandwidth. Flexible PCIe expansion is needed for AI accelerators and frame grabbers. Reliable long-term operation and multiple display interfaces are also crucial. Remote management capabilities are becoming increasingly valuable too. They enable hospitals to perform remote system monitoring, maintenance, and BIOS updates. This improves service efficiency and minimizes downtime for mission-critical equipment.
The Portwell PCOM-B65C COM Express® Type 6 Compact Module is designed for advanced embedded applications. It supports AI-enabled edge computing perfectly. This module combines powerful computing, AI acceleration, and rich I/O expansion. All of this is packed into a compact 95 × 95 mm form factor.
AMD Ryzen™ Embedded 8000 Series processors power the PCOM-B65C. It integrates up to 8 Zen 4 CPU cores and RDNA™ 3 graphics. It also features an AMD XDNA™ Neural Processing Unit (NPU). This NPU can deliver up to 16 TOPS of AI performance. This heterogeneous architecture enables real-time AI inference. It also maintains strong graphics performance for medical visualization.
The PCOM-B65C supports up to 96GB of DDR5-5600 ECC/non-ECC memory. It offers PCIe Gen4 expansion and optional onboard PCIe NVMe storage. The module can also drive up to four independent displays. It provides ample bandwidth for image acquisition and AI-assisted analysis. Its configurable 15W–54W TDP is another great feature. It allows designers to optimize performance and thermal design for various medical equipment.
Beyond computing performance, the PCOM-B65C integrates Out-of-Band (OOB) Management. This enables remote power control, system reboot, and BIOS updates. This capability helps manufacturers reduce maintenance costs. It also improves equipment availability across distributed healthcare environments.
AI continues to redefine modern healthcare. Because of this, embedded computing platforms must provide more than raw processing power. They must deliver scalability, reliability, security, and long-term support. The Portwell PCOM-B65C meets these evolving requirements. It combines AI acceleration, advanced graphics, and flexible expansion with industrial-grade reliability.
The PCOM-B65C can be deployed in AI-assisted medical imaging or diagnostic equipment. It is also ideal for patient monitoring systems and surgical visualization. It enables OEMs to accelerate product development. They can build scalable healthcare solutions that adapt to evolving clinical needs. Portwell remains committed to continuous innovation in embedded computing. We help manufacturers develop reliable, high-performance systems for the future of medical AI.
