# RadOnc-GPT: An Autonomous LLM Agent for Real-Time Patient Outcomes Labeling at Scale

arXiv: https://arxiv.org/abs/2509.25540 · HTML: https://mlsanity.com/paper/2509.25540/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 38 references that are in the map.
- First posted on arXiv 29 Sep 2025.

## Builds on (most cited first, 9 of 9)

- [Matching Patients to Clinical Trials with Large Language Models](https://mlsanity.com/paper/2307.15051/) (2023)
- [Evaluating Large Language Models on a Highly-specialized Topic, Radiation Oncology Physics](https://mlsanity.com/paper/2304.01938/) (2023)
- [Radiology-GPT: A Large Language Model for Radiology](https://mlsanity.com/paper/2306.08666/) (2023)
- [Exploring the Trade-Offs: Unified Large Language Models vs Local Fine-Tuned Models for Highly-Specific Radiology NLI Task](https://mlsanity.com/paper/2304.09138/) (2023)
- [LLM-driven Multimodal Target Volume Contouring in Radiation Oncology](https://mlsanity.com/paper/2311.01908/) (2023)
- [A recent evaluation on the performance of LLMs on radiation oncology physics using questions of randomly shuffled options](https://mlsanity.com/paper/2412.10622/) (2024)
- [PRISM: Patient Records Interpretation for Semantic Clinical Trial Matching using Large Language Models](https://mlsanity.com/paper/2404.15549/) (2024)
- [End-To-End Clinical Trial Matching with Large Language Models](https://mlsanity.com/paper/2407.13463/) (2024)
- [Fine-Tuning Open-Source Large Language Models to Improve Their Performance on Radiation Oncology Tasks: A Feasibility Study to Investigate Their Potential Clinical Applications in Radiation Oncology](https://mlsanity.com/paper/2501.17286/) (2025)

## Built on by (most cited first, 5 of 5)

- [The Path to Self-Evolving Clinical Systems: Scaling Medical Agents from Assistance to Autonomy](https://mlsanity.com/paper/2607.11175/) (2026)
- [The Daily Dose: Workflow-Integrated Large Language Model Automation for Clinical Summarization and Trial Identification in Radiation Oncology](https://mlsanity.com/paper/2605.26346/) (2026)
- [Exploration of Foundation Model-Based Robots in Patient and Elderly Care](https://mlsanity.com/paper/2606.10208/) (2026)
- [Sense and Sensitivity: Benchmarking LLM Clinical Triage Recommendations with Physician Experts](https://mlsanity.com/paper/2609.38600/) (2026)
- [RadOnc-Agent: An LLM-Orchestrated Framework for AI Workflows Across the Radiotherapy Care Pathway](https://mlsanity.com/paper/2610.06923/) (2026)

## Abstract (arXiv)

Manual labeling limits the scale, accuracy, and timeliness of patient outcomes research in radiation oncology. We present RadOnc-GPT, an autonomous large language model (LLM)-based agent capable of independently retrieving patient-specific information, iteratively assessing evidence, and returning structured outcomes. Our evaluation explicitly validates RadOnc-GPT across two clearly defined tiers of increasing complexity: (1) a structured quality assurance (QA) tier, assessing the accurate retrieval of demographic and radiotherapy treatment plan details, followed by (2) a complex clinical outcomes labeling tier involving determination of mandibular osteoradionecrosis (ORN) in head-and-neck cancer patients and detection of cancer recurrence in independent prostate and head-and-neck cancer cohorts requiring combined interpretation of structured and unstructured patient data. The QA tier establishes foundational trust in structured-data retrieval, a critical prerequisite for successful complex clinical outcome labeling.