癌症是一种异质性、多方面的疾病，在全球范围内具有重要影响。尽管抗击癌症的技术取得了巨大进步，但早期诊断和选择有效的治疗方法仍然是一个挑战。由于包含多级数据的大规模数据集的便利性，需要新的生物信息学工具将这些丰富的信息转化为临床上有用的决策支持工具。在这一领域，人工智能（AI）技术以其高度多样化的应用正在迅速普及。机器学习方法，如贝叶斯网络、支持向量机、决策树、随机森林、梯度提升和 K 最近邻，包括深度学习等神经网络模型，已被证明在预测、预后和诊断研究中有价值。研究人员最近采用大型语言模型来解决新维度的问题。然而，在临床环境中利用人工智能的机会需要克服重大障碍——一个主要问题是缺乏可用的报告指南，阻碍了已发表研究的可重复性。在这篇综述中，我们讨论了人工智能方法的应用，并探讨了它们的优点和局限性。我们总结了人工智能在医疗保健领域的可用指南，并强调了人工智能模型对癌症研究未来方向的潜在作用和影响。© 2024。作者。

Cancer is a heterogeneous and multifaceted disease with a significant global footprint. Despite substantial technological advancements for battling cancer, early diagnosis and selection of effective treatment remains a challenge. With the convenience of large-scale datasets including multiple levels of data, new bioinformatic tools are needed to transform this wealth of information into clinically useful decision-support tools. In this field, artificial intelligence (AI) technologies with their highly diverse applications are rapidly gaining ground. Machine learning methods, such as Bayesian networks, support vector machines, decision trees, random forests, gradient boosting, and K-nearest neighbors, including neural network models like deep learning, have proven valuable in predictive, prognostic, and diagnostic studies. Researchers have recently employed large language models to tackle new dimensions of problems. However, leveraging the opportunity to utilize AI in clinical settings will require surpassing significant obstacles-a major issue is the lack of use of the available reporting guidelines obstructing the reproducibility of published studies. In this review, we discuss the applications of AI methods and explore their benefits and limitations. We summarize the available guidelines for AI in healthcare and highlight the potential role and impact of AI models on future directions in cancer research.© 2024. The Author(s).