The role of machine learning (a part of artificial intelligence—AI) in the diagnosis and treatment of various types of oncology is steadily increasing. It is expected that the use of AI in oncology will speed up both diagnostic and treatment planning processes. This review describes recent applications of machine learning in oncology, including medical image analysis, treatment planning, patient survival prognosis, and the synthesis of drugs at the point of care. The fast and reliable analysis of medical images is of great importance in the case of fast-flowing forms of cancer. The introduction of ML for the analysis of constantly growing volumes of big data makes it possible to improve the quality of prescribed treatment and patient care. Thus, ML is expected to become an essential technology for medical specialists. The ML model has already improved prognostic prediction for patients compared to traditional staging algorithms. The direct synthesis of the necessary medical substances (small molecule mixtures) at the point of care could also seriously benefit from the application of ML. We further review the main trends in the use of artificial intelligence-based technologies in modern oncology. This review demonstrates the future prospects of using ML tools to make progress in cancer research, as well as in other areas of medicine. Despite growing interest in the use of modern computer technologies in medical practice, a number of unresolved ethical and legal problems remain. In this review, we also discuss the most relevant issues among them.
机器学习（人工智能AI的一部分）在各类肿瘤诊断与治疗中的作用正稳步提升。预计AI在肿瘤学领域的应用将加速诊断与治疗规划流程。本综述阐述了机器学习在肿瘤学中的最新应用，涵盖医学影像分析、治疗方案制定、患者生存预后及床旁药物合成等方面。对于快速进展型癌症而言，医学影像的快速精准分析至关重要。引入机器学习技术分析持续增长的大数据量，有助于提升治疗方案制定与患者护理的质量。因此，机器学习有望成为医疗专业人员不可或缺的核心技术。相较于传统分期算法，机器学习模型已展现出更优越的患者预后预测能力。在床旁直接合成所需医疗物质（小分子混合物）的领域，机器学习同样具有重要应用潜力。本文进一步综述了基于人工智能的技术在现代肿瘤学应用中的主要趋势，展示了机器学习工具在癌症研究及其他医学领域的发展前景。尽管现代计算机技术在医疗实践中的应用日益受到关注，但仍存在诸多未解决的伦理与法律问题。本综述亦对其中最具现实意义的相关议题进行了探讨。
肿瘤(癌症)患者之家