How AI + Hardware can Transforming Point-of-Care Workflows
Demonstrates custom CM5 and RP2040 embedded hardware paired with local speech and language models for automated offline clinical data logging.
Summary
Bedside Clinical Computing Challenges
Clinical environments produce continuous streams of patient telemetry, diagnostic imaging, and physician notes. However, strict healthcare privacy rules and network instability complicate cloud-based AI assistance at the bedside.
Hardware Architecture: CM5, RP2040, and E-Ink
PamirAI engineers created a dedicated bedside device combining a Raspberry Pi Compute Module 5, an RP2040 microcontroller, and an ultra-low-power E-ink display to run local inference within a tight 10-watt power envelope.
Software Layer and MCP Hub Integration
The embedded software stack features an MCP Hub that translates natural language clinician requests into automated sensor logging and structured record updates without requiring external network connectivity.
Rapid Sensor Integration via Local AI
Leveraging on-device AI models enables rapid integration and calibration of medical telemetry sensors, empowering clinicians with immediate, automated documentation right at the point of care.
Related talks
Creator Stage·21 min
Kevin · Tianqi
Presents a pocket-sized 10-watt bedside inference device running local 3B language models on a CM5 and RP2040 for private clinical workflows.
Creator Stage·22 min
Dr Katie Paxton Fear
Demonstrates using AI-assisted coding to reverse engineer conventional hardware and integrate legacy sensors into Home Assistant.
Village Interviews·9 min
The DEF CON Video Team gets the full overview of hands-on hardware labs, firmware analysis, and microelectronics at the Embedded Systems Village.