mlsanityData to 13 Oct 2026

Automated radiotherapy treatment planning guided by GPT-4Vision

arXiv 2406.15609 ↗First posted 21 Jun 2024physics.med-ph cs.AIcited by 12

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Abstract (arXiv)

Objective: Radiotherapy treatment planning is a time-consuming and potentially subjective process that requires the iterative adjustment of model parameters to balance multiple conflicting objectives. Recent advancements in frontier Artificial Intelligence (AI) models offer promising avenues for addressing the challenges in planning and clinical decision-making. This study introduces GPT-RadPlan, an automated treatment planning framework that integrates radiation oncology knowledge with the reasoning capabilities of large multi-modal models, such as GPT-4Vision (GPT-4V) from OpenAI. Approach: Via in-context learning, we incorporate clinical requirements and a few (3 in our experiments) approved clinical plans with their optimization settings, enabling GPT-4V to acquire treatment planning domain knowledge. The resulting GPT-RadPlan system is integrated into our in-house inverse treatment planning system through an application programming interface (API). For a given patient, GPT-RadPlan acts as both plan evaluator and planner, first assessing dose distributions and dose-volume histograms (DVHs), and then providing textual feedback on how to improve the plan to match the physician's requirements. In this manner, GPT-RadPlan iteratively refines the plan by adjusting planning parameters, such as weights and dose objectives, based on its suggestions. Main results: The efficacy of the automated planning system is showcased across 17 prostate cancer and 13 head and neck cancer VMAT plans with prescribed doses of 70.2 Gy and 72 Gy, respectively, where we compared GPT-RadPlan results to clinical plans produced by human experts. In all cases, GPT-RadPlan either outperformed or matched the clinical plans, demonstrating superior target coverage and reducing organ-at-risk doses by 5 Gy on average (15 percent for prostate and 10-15 percent for head and neck).

Key facts. Cited by 12 papers in the map (data through 13 Oct 2026). Builds on 10 of its 42 references that are in the map. First posted on arXiv 21 Jun 2024.

Builds on

10 of 42 references.

References · 10

  1. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,656
  2. 2019Optuna: A Next-generation Hyperparameter Optimization Frameworkcited by 2,472
  3. 2023In-context Vectors: Making In Context Learning More Effective and Controllable Through Latent Space Steeringcited by 243
  4. 2024Reinforcement learningcited by 238
  5. 2023Calibrating LLM-Based Evaluatorcited by 53
  6. 2022Avoiding spurious correlations via logit correctioncited by 32
  7. 2023RadOnc-GPT: A Large Language Model for Radiation Oncologycited by 18
  8. 2021Deep Probability Estimationcited by 14
  9. 2018Three-Dimensional Dose Prediction for Lung IMRT Patients with Deep Neural Networks: Robust Learning from Heterogeneous Beam Configurationscited by 12
  10. 2022Learning Image Representations for Content Based Image Retrieval of Radiotherapy Treatment Planscited by 2

Built on by

Cited by year (2026 to 13 Oct)

Citing papers per year: 2024: 2; 2025: 8; 2026: 2.

12 papers.

  1. 2024Reducing Hallucinations in Vision-Language Models via Latent Space Steeringcited by 57
  2. 2025Medical Reasoning in the Era of LLMs: A Systematic Review of Enhancement Techniques and Applicationscited by 10
  3. 2024Enabling Scalable Evaluation of Bias Patterns in Medical LLMscited by 5
  4. 2025Autonomous Radiotherapy Treatment Planning Using DOLA: A Privacy-Preserving, LLM-Based Optimization Agentcited by 4
  5. 2025MedPlan: A Two-Stage RAG-Based System for Personalized Medical Plan Generationcited by 4
  6. 2025Zero-Shot Large Language Model Agents for Fully Automated Radiotherapy Treatment Planningcited by 1
  7. 2025Benchmarking GPT-5 in Radiation Oncology: Measurable Gains, but Persistent Need for Expert Oversightcited by 0
  8. 2025An Iterative LLM Framework for SIBT utilizing RAG-based Adaptive Weight Optimizationcited by 0
  9. 2025An Automated Retrieval-Augmented Generation LLaMA-4 109B-based System for Evaluating Radiotherapy Treatment Planscited by 0
  10. 2025Towards Human-Centric Intelligent Treatment Planning for Radiation Therapycited by 0
  11. 2026A Machine-to-Machine Knowledge-Guided LLM Agent for Generalizable Radiotherapy Treatment Planningcited by 0
  12. 2026CAREAgent: Clinical Agent with Structured Reasoning and Tool-Integrated for Order Generationcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025Automated stereotactic radiosurgery planning using a human-in-the-loop reasoning large language model agent
  2. 2026RadOnc-Agent: An LLM-Orchestrated Framework for AI Workflows Across the Radiotherapy Care Pathway
  3. 2025RadOnc-GPT: An Autonomous LLM Agent for Real-Time Patient Outcomes Labeling at Scale
  4. 2026Any2Any 3D Diffusion Models with Knowledge Transfer: A Radiotherapy Planning Study
  5. 2024Large Language Model-Augmented Auto-Delineation of Treatment Target Volume in Radiation Therapy
  6. 2024Mixture of Multicenter Experts in Multimodal AI for Debiased Radiotherapy Target Delineation
  7. 2023Enhancing Medical Task Performance in GPT-4V: A Comprehensive Study on Prompt Engineering Strategies
  8. 2023DiffDP: Radiotherapy Dose Prediction via a Diffusion Model