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.

近期人工智能在医疗领域有了显著进展，本研究提出 RO-LLaMA，一个通用的大型语言模型，特为放射肿瘤学领域量身定制，能够在临床报告总结、放疗方案建议等任务中表现出卓越的性能，并且通过 CEFTune 技术进一步提高了模型的鲁棒性，同时保持了处理干净输入的能力，还将此概念创造性地转化为 LLM 驱动的分割框架 CESEG。实验结果表明，RO-LLaMA 在多中心队列数据集上展现了出色的性能和泛化能力。