高 杨

计算机断层成像（computed tomography，CT）由连续断层图像构成，既能显示局部病灶，也保留器官及邻近组织之间的空间关系。传统人工智能多围绕单病种、单任务建立模型，对完整三维体积以及影像、报告和临床资料的联合利用仍较有限。近年来，视觉基础模型、视觉—语言模型和医学大语言模型借助大规模预训练、三维建模和影像—文本对齐，使CT分析逐步延伸至多器官异常识别、报告生成、病例分诊、风险分层和预后预测。本文围绕CT大模型的技术基础与临床应用展开讨论，同时分析数据异质性、生成错误、评价方法、隐私保护和可信部署等现实问题。现有证据表明，CT大模型现阶段更适合作为医师监督下的信息整合与决策支持工具，其临床获益仍需多中心前瞻性研究及真实世界数据进一步验证。

人工智能大模型；CT影像；影像理解；多模态学习；临床决策支持

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