Welcome to Visited Lingnan Modern Clinics In Surgery, Today is

Lingnan Modern Clinics In Surgery ›› 2026, Vol. 26 ›› Issue (02): 131-139.DOI: 10.3969/j.issn.1009-976X.2026.02.009

• Review • Previous Articles    

“Intelligent” treatment for thyroid cancer: application and prospect of artificial intelligence in thyroid cancer diagnosis and treatment

NUERBANNU Tabusibieke, SHAO Guoan*   

  1. Department of Thyroid and Breast Surgery, The Fifth Affiliated Hospital of Xinjiang Medical University,Urumqi 830011, China
  • Contact: * SHAO Guoan, 3236875377@qq.com

“智”疗甲状腺癌:人工智能在甲状腺癌诊疗中的应用与展望

努尔班努·塔布斯别克, 邵国安*   

  1. 新疆医科大学第五附属医院甲乳外科,乌鲁木齐 830011
  • 通讯作者: *邵国安,Email:3236875377@qq.com

Abstract: Thyroid cancer (TC) is the most common malignant tumor of the endocrine system, with its incidence continuously rising worldwide. Although most patients have a favorable prognosis, challenges remain, including insufficient accuracy in early diagnosis, inaccurate assessment of lymph node metastasis, difficulties in effectively implementing individualized treatment, and low follow-up efficiency. In recent years, the rapid development of artificial intelligence (AI) technology has brought transformative changes to the precise diagnosis and treatment of TC. This review primarily focuses on the application of AI in TC diagnosis, outlining the evolution of AI technologies and comparing the efficacy of different methods in distinguishing benign from malignant lesions, predicting lymph node metastasis, and molecular subtyping. Simultaneously, it explores the potential of AI in treatment decision-making, prognosis assessment, and follow-up management, analyzes current challenges, and aims to promote the translation of AI technology into clinical practice.

Key words: thyroid cancer, artificial intelligence, surgical decision, deep learning, precise diagnosis

摘要: 甲状腺癌(TC)是内分泌系统最常见的恶性肿瘤,全球范围内发病率持续上升。虽然大多数患者预后状况良好,但仍面临早期诊断准确性不足、淋巴结转移评估不准确、个体化治疗难以有效开展以及随访效率低等问题。近年来,人工智能(AI)技术快速发展,为TC精准诊疗带来了变革。本综述主要聚焦AI在TC诊断中的应用,梳理了AI技术的演进历程,比较了不同方法在良恶性鉴别、淋巴结转移预测及分子分型中的效能。同时,探讨了AI在治疗决策、预后评估及随访管理中的应用潜力,分析了当前面临的挑战,旨在推动AI技术向临床应用转化。

关键词: 甲状腺癌, 人工智能, 深度学习, 外科决策, 精准诊断

CLC Number: