人工智能（AI）是一种具有变革性的技术，它吸引了公众的想象力，可以彻底改变生物医学领域。AI和机器学习（ML）算法有可能突破肿瘤学研究和实践中的现有障碍，例如自动化工作流程、个性化护理和减少医疗保健不平等。文献中新兴的AI/ML应用包括癌症的筛查和早期检测、疾病诊断、治疗反应预测、预后和加速药物发现。尽管存在这种激动，仅有少数AI/ML模型已经得到适当验证，并且更少的模型成为了常规临床使用的监管产品。在此综述中，我们重点介绍了阻碍AI/ML临床转化的主要挑战。我们提供了放射学、放射肿瘤学、免疫疗法和肿瘤药物发现领域的不同临床应用案例。我们分析了每个案例所涉及的独特挑战和机会。最后，我们总结了成功实施AI/ML的一般要求，重点介绍了特定的例子和强调点，包括多学科合作的利益相关者、领域专家在AI增强中的作用、AI/ML模型的透明度，以及建立全面的质量保证计划以减轻训练偏见和数据漂移的风险，最终实现更安全、更有益的AI/ML应用于肿瘤实验室和临床医疗。© 2023. The Author(s), under exclusive licence to Springer Nature Limited.

Artificial intelligence (AI) is a transformative technology that is capturing popular imagination and can revolutionize biomedicine. AI and machine learning (ML) algorithms have the potential to break through existing barriers in oncology research and practice such as automating workflow processes, personalizing care, and reducing healthcare disparities. Emerging applications of AI/ML in the literature include screening and early detection of cancer, disease diagnosis, response prediction, prognosis, and accelerated drug discovery. Despite this excitement, only few AI/ML models have been properly validated and fewer have become regulated products for routine clinical use. In this review, we highlight the main challenges impeding AI/ML clinical translation. We present different clinical use cases from the domains of radiology, radiation oncology, immunotherapy, and drug discovery in oncology. We dissect the unique challenges and opportunities associated with each of these cases. Finally, we summarize the general requirements for successful AI/ML implementation in the clinic, highlighting specific examples and points of emphasis including the importance of multidisciplinary collaboration of stakeholders, role of domain experts in AI augmentation, transparency of AI/ML models, and the establishment of a comprehensive quality assurance program to mitigate risks of training bias and data drifts, all culminating toward safer and more beneficial AI/ML applications in oncology labs and clinics.© 2023. The Author(s), under exclusive licence to Springer Nature Limited.