The successful use of artificial intelligence (AI) for diagnostic purposes has prompted the application of AI-based cancer imaging analysis to address other, more complex, clinical needs. In this Perspective, we discuss the next generation of challenges in clinical decision-making that AI tools can solve using radiology images, such as prognostication of outcome across multiple cancers, prediction of response to various treatment modalities, discrimination of benign treatment confounders from true progression, identification of unusual response patterns and prediction of the mutational and molecular profile of tumours. We describe the evolution of and opportunities for AI in oncology imaging, focusing on hand-crafted radiomic approaches and deep learning-derived representations, with examples of their application for decision support. We also address the challenges faced on the path to clinical adoption, including data curation and annotation, interpretability, and regulatory and reimbursement issues. We hope to demystify AI in radiology for clinicians by helping them to understand its limitations and challenges, as well as the opportunities it provides as a decision-support tool in cancer management.

人工智能（AI）在诊断领域的成功应用促使基于人工智能的癌症成像分析的应用来满足其他更复杂的临床需求。在本视角中，我们讨论人工智能工具可以使用放射学图像解决临床决策中的下一代挑战，例如预测多种癌症的结果、预测对各种治疗方式的反应、区分良性治疗与真实进展的混杂因素，识别异常反应模式并预测肿瘤的突变和分子谱。我们描述了人工智能在肿瘤成像领域的发展和机遇，重点关注手工制作的放射组学方法和深度学习衍生的表示，以及它们在决策支持中的应用示例。我们还解决临床采用过程中面临的挑战，包括数据管理和注释、可解释性以及监管和报销问题。我们希望通过帮助临床医生了解人工智能的局限性和挑战，以及它作为癌症管理决策支持工具所提供的机会，为临床医生揭开放射学人工智能的神秘面纱。