[Submitted on 17 Oct 2026]
Title:MedPAO-Assist: Edge-Deployable, Modality-Aware Agentic AI for Radiology Report Structuring
View a PDF of the paper titled MedPAO-Assist: Edge-Deployable, Modality-Aware Agentic AI for Radiology Report Structuring, by Shrish Shrinath Vaidya and 2 other authors
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Abstract:The adoption of Large Language Models for radiology report structuring has been largely dominated by either proprietary cloud-based models or large open models requiring high-end GPUs for deployment, which is a critical bottleneck for clinical adoption in radiology workflows that demand data privacy and offline operation. To address this, we present MedPAO-Assist, an edge-deployable agentic framework that generates structured radiology reports directly from radiologist dictation and voice commands, requiring only $\approx3.3$ GB peak RAM and achieving an average end-to-end latency of $\approx31$ seconds on a standard laptop CPU through an asynchronous, multi-threaded execution pipeline. The system currently supports four major imaging modalities -- Chest X-ray, Chest CT, Abdomen CT, and Brain MRI. To our knowledge, it is among the earliest agentic systems to run its entire pipeline with a Small Language Model (SLM) in the 0.6B-parameter range. This multi-agent architecture, built around an Extract-Ground-Generate paradigm, performs concept extraction, SNOMED-CT grounding, and structured report generation, followed by a speech-driven human-in-the-loop correction phase, together forming a fully "no-touch" reporting workflow that requires no keyboard input, external API calls, or internet connectivity. We evaluate each pipeline stage individually -- concept extraction, ontology grounding, and concept categorization and report hardware profiling results demonstrating the system's feasibility for real-time, CPU-only clinical deployment.
Comments: The paper has been accepted at MICCAI Workshop Agentic AI for Medicine, 2026
Subjects:
Artificial Intelligence (cs.AI)
Cite as: arXiv:2610.26913 [cs.AI]
(or arXiv:2610.26913v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2610.26913
arXiv-issued DOI via DataCite
Submission history
From: Shrish Shrinath Vaidya [view email] [v1] Sat, 17 Oct 2026 06:13:06 UTC (6,079 KB)
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