AI

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

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Based on a feed excerpt; full article content has not been acquired. Source evidence retrieved 21/10/2026, 04:03:24 UTC.

Briefings

MedPAO-Assist is an edge-deployable agentic AI framework for radiology report structuring, enabling offline operation with minimal hardware requirements. It processes four imaging modalities (Chest X-ray, Chest CT, Abdomen CT, Brain MRI) using a Small Language Model (SLM) with approximately 0.6B parameters. The system executes an Extract-Ground-Generate pipeline with an average end-to-end latency of approximately 31 seconds on a standard laptop CPU, achieving peak RAM usage of approximately 3.3 GB. The workflow includes concept extraction, SNOMED-CT grounding, report generation, and a speech-driven human-in-the-loop correction phase, eliminating the need for keyboard input, external APIs, or internet connectivity.

Key points

• MedPAO-Assist is an edge-deployable agentic framework that processes four major imaging modalities: Chest X-ray, Chest CT, Abdomen CT, and Brain MRI.
• The system achieves an average end-to-end latency of approximately 31 seconds on a standard laptop CPU while requiring only approximately 3.3 GB peak RAM.

Context

This research addresses the critical bottleneck of high-end GPU requirements for radiology report structuring, offering a solution that enables data privacy and offline operation essential for clinical adoption.

Limitations

The evidence is limited to a single preprint version (arXiv:2610.26913v1) from October 2026, with no additional validation, clinical trial data, or long-term performance metrics provided.

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