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 ≈3.3 GB peak RAM and achieving an average end-to-end latency of ≈31 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.
Links to Paper and Supplementary Materials
Main Paper (Open Access Version): https://papers.miccai.org/miccai-2026-sat/paper/MedAgent_025.pdf
SharedIt Link: Not yet available
SpringerLink (DOI): Not yet available
Supplementary Material: Not Submitted
Link to Open Review
Open Review Page: https://openreview.net/forum?id=l8Ap8x1ORx
BibTex
@InProceedings{VaiShr_MedPAOAssist_MICCAISAT2026,
author = { Vaidya, Shrish Shrinath AND Selvam, Minmini AND Krishnamurthi, Ganapathy},
title = { { MedPAO-Assist: Edge-Deployable, Modality-Aware Agentic AI for Radiology Report Structuring } },
booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2026 Workshops and Challenges},
year = {2026},
publisher = {Springer Nature Switzerland},
volume = {LNCS 17263},
month = {pending},
page = {pending}
}