MedPAO-Assist: Edge-Deployable, Modality-Aware Agentic AI for Radiology Report Structuring
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You can now run a complete, offline clinical structuring agent pipeline on a standard laptop CPU using a 0.6B-parameter model that requires just 3.3 GB of peak RAM and averages 31 seconds of latency. This enables you to deploy zero-connectivity, speech-driven extraction and SNOMED-CT grounding workflows directly on edge devices without relying on high-end GPUs or external APIs. It proves that highly specialized agentic pipelines can be shrunk to run locally in data-sensitive environments, dramatically cutting operational cloud costs and eliminating network latency bottlenecks.
MedPAO-Assist enables radiology report structuring using a Small Language Model (SLM) with only 0.6B parameters, achieving real-time performance on a standard laptop CPU with ≈3.3 GB peak RAM and ≈31-second latency. This edge-deployable, modality-aware system eliminates reliance on high-end GPUs or cloud APIs, making it feasible for offline, privacy-sensitive clinical workflows while reducing hardware costs and operational complexity.