癌症的个性化治疗策略经常依赖于分子生物学检测确定的基因改变。历史上，这些过程通常需要单基因测序、下一代测序或有经验的病理学家在临床环境中对组织病理学切片进行视觉检查。在过去的十年里，人工智能技术的进步在癌症图像识别任务中展现了显著的潜力，帮助医生进行准确的诊断。同时，AI技术使得可以整合放射学、组织学和基因组学等多模态数据，为精准治疗背景下的患者分层提供了关键的指导。考虑到基因突变检测对许多患者来说是负担不起且耗时的，基于常规临床放射学扫描和组织全切片图像的AI方法预测基因突变已经成为实际临床实践中的热门问题。在本综述中，我们综合了超越标准技术的分子智能诊断的一般框架。然后，我们总结了AI在肺癌、脑癌、乳腺癌和其他肿瘤类型的放射学和组织学成像相关的突变和分子特征预测方面新兴的应用。此外，我们得出结论，AI技术在医学领域实际应用中确实存在多个挑战，包括数据整理、特征融合、模型可解释性和实践规定等方面。但是，尽管存在这样的挑战，我们仍然看好将AI作为一种高度潜在的决策支持工具帮助肿瘤学家未来治疗管理的临床实施。版权所有 © 2023作者。由Elsevier Ltd.出版。保留所有权利。

Personalized treatment strategies for cancer frequently rely on the detection of genetic alterations which are determined by molecular biology assays. Historically, these processes typically require single-gene sequencing, next-generation sequencing, or visual inspection of histopathology slides by experienced pathologists in the clinical context. In the past decade, advances in artificial intelligence (AI) technologies have demonstrated remarkable potential in oncology image-recognition tasks assisting physicians in accurate diagnosis. Meanwhile, AI techniques make it possible to integrate multimodal data such as radiology, histology, and genomics which provides critical guidance for the stratification of patients in the context of precision therapy. Given that the mutation detection is unaffordable and time-consuming for a considerable number of patients, predicting gene mutations based upon routine clinical radiological scans and whole-slide images of tissue with AI-based methods has become a hot issue in actual clinical practice. In this review, we synthesize the general framework of molecular intelligent diagnostics beyond standard techniques. Then we summarize the emerging applications of AI in the prediction of mutational and molecular profiles of common cancers (lung, brain, breast, and other tumor types) pertaining to radiology and histology imaging. Furthermore, we concluded that there truly exist multiple challenges of AI techniques in the way of its real-world application in the medical field, including data curation, feature fusion, model interpretability, and practice regulations. However, despite that, we still prospect the clinical implementation of AI as a highly potential decision-support tool to aid oncologists in future cancer treatment management.Copyright © 2023 The Authors. Published by Elsevier Ltd.. All rights reserved.