放射治疗计划是一个耗时且潜在主观的过程，需要迭代调整模型参数以平衡多个相互冲突的目标。大型基础模型的最新进展为解决规划和临床决策中的挑战提供了有希望的途径。本研究引入了 GPT-RadPlan，这是一个完全自动化的治疗计划框架，它利用多模态大语言模型中编码的先前放射肿瘤学知识，例如 OpenAI 的 GPT-4Vision (GPT-4V)。 GPT-RadPlan 意识到规划协议是背景，并充当专家人类规划师，能够指导治疗规划过程。通过情境学习，我们将不同疾病部位的临床方案作为提示，使 GPT-4V 能够获取治疗计划领域知识。由此产生的 GPT-RadPlan 代理通过 API 集成到我们内部的逆向治疗计划系统中。使用多个前列腺和头部展示了自动规划系统的功效

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 large foundation models offer promising avenues for addressing the challenges in planning and clinical decision-making. This study introduces GPT-RadPlan, a fully automated treatment planning framework that harnesses prior radiation oncology knowledge encoded in multi-modal large language models, such as GPT-4Vision (GPT-4V) from OpenAI. GPT-RadPlan is made aware of planning protocols as context and acts as an expert human planner, capable of guiding a treatment planning process. Via in-context learning, we incorporate clinical protocols for various disease sites as prompts to enable GPT-4V to acquire treatment planning domain knowledge. The resulting GPT-RadPlan agent is integrated into our in-house inverse treatment planning system through an API. The efficacy of the automated planning system is showcased using multiple prostate and head & neck cancer cases, where we compared GPT-RadPlan results to clinical plans. In all cases, GPT-RadPlan either outperformed or matched the clinical plans, demonstrating superior target coverage and organ-at-risk sparing. Consistently satisfying the dosimetric objectives in the clinical protocol, GPT-RadPlan represents the first multimodal large language model agent that mimics the behaviors of human planners in radiation oncology clinics, achieving remarkable results in automating the treatment planning process without the need for additional training.