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人工智能用于肝细胞癌临床诊断及预后的研究进展 被引量:1

Research progress on application of artificial intelligence in clinical diagnosis and prognosis of patients with hepatocellular carcinoma
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摘要 肝细胞癌(hepatocellular carcinoma,HCC)是临床常见的消化系统恶性肿瘤,其早期诊断困难且预后较差。近年来,随着人工智能(artificial intelligence,AI)技术的飞速发展,其在改进HCC诊疗方面的价值也得到了体现。基于机器学习及深度学习的AI模型可以整合影像学、血清学及肠道菌群等多模态数据,从而提高HCC早期风险预测和病灶检测能力。此外,AI模型也能够通过特定的算法为介入治疗和药物治疗方案提供决策参考,并改进对HCC患者临床预后评估,从而使患者获得生存获益。虽然AI技术的发展极快,但是目前仍不能替代临床医生。随着如ChatGPT及Deepseek等划时代AI算法工具的出现,相信AI能够成为临床医生治疗HCC的强大武器,从而使更多患者从中获益。 Hepatocellular carcinoma(HCC)is a common digestive system malignancy with challenging early diagnosis and poor prognosis.In recent years,the rapid development of artificial intelligence(AI)has demonstrated significant potential in improving HCC diagnosis and treatment.Machine learning(ML)and deep learning(DL)-based AI models can integrate multimodal data,including imaging,serological markers,and gut microbiota,thereby enhancing early risk prediction and lesion detection for HCC.Additionally,AI models provide decision support for interventional and pharmacological therapies through specific algorithms,while improving clinical prognosis assessment to optimize patient survival outcomes.Although AI cannot yet replace clinicians,the emergence of groundbreaking tools such as ChatGPT and DeepSeek suggests that AI will become a powerful adjunct in HCC management.This review summarizes recent advancements in AI-assisted HCC diagnosis,treatment decision-making,and prognosis evaluation,so as to provide insights for broader clinical applications of AI in HCC.
作者 朱鹏 覃刚 郭世民 Zhu Peng;Qin Gang;Guo Shimin(Department of Gastroenterology,Suining First People's Hospital,Suining 629000,China;Department of Infectious Diseases,989th Hospital of Joint Logistic Support Force,Luoyang 471000,China)
出处 《国际医药卫生导报》 2025年第12期1979-1983,共5页 International Medicine and Health Guidance News
基金 肝胆相照公益基金会人工肝专项基金(iGandanF-1082024-RGG081)。
关键词 肝细胞癌 人工智能 机器学习 深度学习 诊断 治疗 预后 进展 Hepatocellular carcinoma Artificial intelligence Machine learning Deep learning Diagnosis Treatment Prognosis Progress
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