RadOnc-Agent is an AI framework that organizes radiotherapy into four clinical phases and offers 26 callable functions via a conversational interface. A large‑language‑model controller maps clinical intent to schema‑constrained calls, maintains patient and workflow context, and routes requests to specialist services. In evaluations, the system achieved high accuracy in single‑function calls (98.79%) and cross‑stage workflow completions (96.50% scripted, 96.67% real‑patient), demonstrating technical feasibility of LLM‑orchestrated coordination across heterogeneous radiotherapy capabilities.
By Caiwen Jiang, Shuoyang Wei, Songlin Zhao, Junyu Li, Jingyuan Chen, Wei Liu
arXiv:2610.15714v1 Announce Type: new
Abstract: Healthcare agents must transform longitudinal clinical evidence into reliable actions through heterogeneous tools and stateful workflows, conditions un...
By Rui Tang, Hongqiu Wang
arXiv:2609.13543v1 Announce Type: new
Abstract: LLM agents are predominantly benchmarked on short, single-task trajectories, yet real deployments run for hours under contention, surfacing a different...
By Grace Chang Yuan, Xiaoman Zhang, Sung Eun Kim, Luyang Luo, Pranav Rajpurkar
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.
By Shrish Shrinath Vaidya, Minmini Selvam, Ganapathy Krishnamurthi
The paper presents an evidence-driven, multi-agent AI system for head and neck proton radiotherapy planning. It combines a DRL-based planning agent that generates multiple treatment plans with an LLM-based conversational agent that refines the chosen plan through natural language interactions. The system, trained on 21 patients, improves instruction-following and domain-specific reasoning, yielding more clinically appropriate trade-offs in patient-specific plan refinement.
By Qingqing Wang, Liqiang Xiao, Chang Chang
arXiv:2606. 01961v1 Announce Type: new Abstract: Autonomous agents are increasingly expected to support end-to-end medical-AI research workflows, moving beyond isolated prediction tasks or short-form clinical question answering.
By Junqi Liu, Salena Song, Yuhan Wang, Jiawei Mao, Hardy Chen, Xiaoke Huang, Tianhao Qi, Pengfei Guo, Yucheng Tang, Yufan He, Can Zhao, Andriy Myronenko, Dong Yang, Daguang Xu, Yuyin Zhou