RESEARCH · RESEARCH · #2333
MedPAO-Assist: edge-deployable agentic system for structured radiology reports using a 0.6B SLM
The arXiv v1 paper presents MedPAO-Assist, an agentic, edge-deployable framework that converts radiologist dictation and voice commands into structured reports for four modalities (Chest X‑ray, Chest CT, Abdomen CT, Brain MRI). The pipeline—an Extract‑Ground‑Generate multi-agent design with SNOMED‑CT grounding and a speech-driven human-in-the-loop correction phase—runs entirely offline on a laptop CPU with ≈3.3 GB peak RAM and ≈31 s end-to-end latency, and the authors report per-stage evaluations and hardware profiling to demonstrate feasibility for CPU-only clinical deployment.
KEY POINTS
- The arXiv v1 paper presents MedPAO-Assist, an agentic, edge-deployable framework that converts radiologist dictation and voice commands into structured reports for four modalities (Chest X‑ray, Chest CT, Abdomen CT, Brain MRI).
- The pipeline—an Extract‑Ground‑Generate multi-agent design with SNOMED‑CT grounding and a speech-driven human-in-the-loop correction phase—runs entirely offline on a laptop CPU with ≈3.3 GB peak RAM and ≈31 s end-to-end latency, and the authors report per-stage evaluations and hardware profiling to demonstrate feasibility for CPU-only clinical deployment.
- Shows that an agentic radiology-reporting pipeline can run offline on commodity CPUs with a small (0.6B) model, lowering privacy, cost, and deployment barriers for clinical use.
WHY IT MATTERS
Shows that an agentic radiology-reporting pipeline can run offline on commodity CPUs with a small (0.6B) model, lowering privacy, cost, and deployment barriers for clinical use.