Recent advancements in Artificial Intelligence (AI) have profoundly influenced medical fields, by providing tools to reduce clinical workloads. However, most AI models are constrained to execute uni-modal tasks, in stark contrast to the comprehensive approaches utilized by medical professionals. To address this, here we present RO-LLaMA, a versatile generalist large language model (LLM) tailored for the field of radiation oncology. This model seamlessly covers a wide range of the workflow of radiation oncologists, adept at various tasks such as clinical report summarization, radiation therapy plan suggestion, and plan-guided therapy target volume segmentation. In particular, to maximize the end-to-end performance, we further present a novel Consistency Embedding Fine-Tuning (CEFTune) technique, which boosts LLM's robustness to additional errors at the intermediates while preserving the capability of handling clean inputs, and creatively transform this concept into LLM-driven segmentation framework as Consistency Embedding Segmentation (CESEG). Experimental results on multi-centre cohort sets demonstrate our proposed RO-LLaMA's promising performance for diverse tasks with generalization capabilities.

翻译：近年来，人工智能的飞速发展通过提供减轻临床工作负担的工具，深刻影响了医学领域。然而，大多数AI模型局限于执行单模态任务，这与医疗专业人员采用的综合性方法形成鲜明对比。为解决这一问题，本文提出RO-LLaMA——一种专为放射肿瘤学领域定制的多功能通用大语言模型。该模型无缝覆盖放射肿瘤医师的广泛工作流程，擅长处理临床报告摘要、放疗计划建议及计划引导的靶区体积分割等多种任务。为最大化端到端性能，我们进一步提出一种新型一致性嵌入微调（CEFTune）技术，该技术提升模型对中间环节误差的鲁棒性，同时保持其处理干净输入的能力，并创造性地将该理念转化为基于LLM的分割框架——一致性嵌入分割（CESEG）。多中心队列数据集上的实验结果表明，所提出的RO-LLaMA在多样化任务中展现出良好的性能与泛化能力。