放射肿瘤学人工通用智能

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
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引用次数: 3

摘要

人工通用智能(AGI)的出现正在改变放射肿瘤学。作为 AGI 的杰出先锋,GPT-4 和 PaLM 2 等大型语言模型 (LLM) 可以处理大量文本,而 Segment Anything Model (SAM) 等大型视觉模型 (LVM) 可以处理大量成像数据,从而提高放射治疗的效率和精确度。本文探讨了 AGI 在放射肿瘤学领域的全方位应用,包括初步咨询、模拟、治疗计划、治疗实施、治疗验证和患者随访。视觉数据与 LLMs 的融合还能创建强大的多模态模型,阐明细微的临床模式。总之,AGI有望推动向数据驱动的个性化放射治疗转变。不过,这些模型应与人类的专业知识和护理相辅相成。本文概述了 AGI 如何改变放射肿瘤学,以提高放射肿瘤学的患者护理标准,其中的关键见解是 AGI 大规模利用多模态临床数据的能力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Artificial general intelligence for radiation oncology

Artificial general intelligence for radiation oncology

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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