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arXiv AI By Shrish Shrinath Vaidya, Minmini Selvam, Ganapathy Krishnamurthi

MedPAO-Assist: Edge-Deployable, Modality-Aware Agentic AI for Radiology Report Structuring

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MedPAO-Assist is an edge‑deployable, agentic AI framework that structures radiology reports from radiologist dictation and voice commands using only about 3.3 GB of RAM and an average latency of 31 seconds on a standard laptop CPU. It supports four imaging modalities—Chest X‑ray, Chest CT, Abdomen CT, and Brain MRI—and operates entirely with a 0.6 B‑parameter small language model, avoiding cloud APIs or high‑end GPUs. The system follows an Extract‑Ground‑Generate pipeline, performing concept extraction, SNOMED‑CT grounding, structured report generation, and a speech‑driven human‑in‑the‑loop correction phase, enabling a fully “no‑touch” reporting workflow that requires no keyboard input or internet connectivity.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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