放射肿瘤学
医学物理学
放射治疗
医学
肿瘤科
内科学
作者
Chenbin Liu,Zhengliang Liu,Jason Holmes,Lu Zhang,Lian Zhang,Yuzhen Ding,Peng Shu,Zihao Wu,Haixing Dai,Yiwei Li,Dinggang Shen,Ninghao Liu,Quanzheng Li,Xiang Li,Dajiang Zhu,Tianming Liu,Wei Liu
出处
期刊:
[Elsevier BV]
日期:2023-11-01
卷期号:1 (3): 100045-100045
被引量:29
标识
DOI:10.1016/j.metrad.2023.100045
摘要
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.
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