Purpose of review 

Current methods for thyroid nodule risk stratification are subjective, and artificial intelligence algorithms have been used to overcome this shortcoming. In this review, we summarize recent developments in the application of artificial intelligence algorithms for estimating the risks of malignancy in a thyroid nodule.

Recent findings 

Artificial intelligence have been used to predict malignancy in thyroid nodules using ultrasound images, cytopathology images, and molecular markers. Recent clinical trials have shown that artificial intelligence model's performance matched that of experienced radiologists and pathologists. Explainable artificial intelligence models are being developed to avoid the black box problem. Risk stratification algorithms using artificial intelligence for thyroid nodules are now commercially available in many countries.

Summary 

Artificial intelligence models could become a useful tool in a thyroidolgist's armamentarium as a decision support tool. Increased adoption of this emerging technology will depend upon increased awareness of the potential benefits and pitfalls in using artificial intelligence.

中文翻译：

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利用人工智能和机器学习评估甲状腺结节的恶性风险

审查目的 

当前的甲状腺结节 风险分层方法是主观的，人工智能算法已被用来克服这一缺点。在这篇综述中，我们总结了人工智能算法在估计甲状腺结节中恶性肿瘤风险中的最新进展。

最近的发现 

人工智能已用于使用超声图像，细胞病理学图像和分子标记物预测甲状腺结节的恶性肿瘤。最近的临床试验表明，人工智能模型的性能与经验丰富的放射科医生和病理学家的性能相匹配。为了避免黑匣子问题，正在开发可解释的人工智能模型。现在，在许多国家/地区都可以使用人工智能技术对甲状腺结节进行风险分层算法。

概要 

人工智能模型可以作为决策支持工具，成为甲状腺科医生武器库中的有用工具。越来越多地采用这种新兴技术将取决于对使用人工智能的潜在好处和陷阱的认识的提高。