Liu Chenbin, Liu Zhengliang, Holmes Jason, Zhang Lu, Zhang Lian, Ding Yuzhen, Shu Peng, Wu Zihao, Dai Haixing, Li Yiwei, Shen Dinggang, Liu Ninghao, Li Quanzheng, Li Xiang, Zhu Dajiang, Liu Tianming, Liu Wei
Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen, Guangdong, China.
School of Computing, University of Georgia, USA.
Meta Radiol. 2023 Nov;1(3). doi: 10.1016/j.metrad.2023.100045. Epub 2023 Nov 24.
The emergence of artificial general intelligence (AGI) is transforming radiation oncology. As prominent vanguards of AGI, large language models (LLMs) such as GPT-4 and PaLM 2 can process extensive texts and large vision models (LVMs) such as the Segment Anything Model (SAM) can process extensive imaging data to enhance the efficiency and precision of radiation therapy. This paper explores full-spectrum applications of AGI across radiation oncology including initial consultation, simulation, treatment planning, treatment delivery, treatment verification, and patient follow-up. The fusion of vision data with LLMs also creates powerful multimodal models that elucidate nuanced clinical patterns. Together, AGI promises to catalyze a shift towards data-driven, personalized radiation therapy. However, these models should complement human expertise and care. This paper provides an overview of how AGI can transform radiation oncology to elevate the standard of patient care in radiation oncology, with the key insight being AGI's ability to exploit multimodal clinical data at scale.
通用人工智能（AGI）的出现正在改变放射肿瘤学。作为AGI的杰出先锋，诸如GPT-4和PaLM 2这样的大型语言模型（LLMs）能够处理大量文本，而诸如分割一切模型（SAM）这样的大型视觉模型（LVMs）能够处理大量成像数据，以提高放射治疗的效率和精度。本文探讨了AGI在放射肿瘤学中的全谱应用，包括初始咨询、模拟、治疗计划、治疗实施、治疗验证和患者随访。视觉数据与大型语言模型的融合还创建了强大的多模态模型，这些模型能够阐明细微的临床模式。总体而言，AGI有望推动向数据驱动的个性化放射治疗转变。然而，这些模型应补充人类的专业知识和护理。本文概述了AGI如何改变放射肿瘤学，以提高放射肿瘤学患者护理的标准，关键在于AGI能够大规模利用多模态临床数据。
