Varlamova Elena V, Butakova Maria A, Semyonova Vlada V, Soldatov Sergey A, Poltavskiy Artem V, Kit Oleg I, Soldatov Alexander V
The Smart Materials Research Institute, Southern Federal University, 178/24 Sladkova Str., 344090 Rostov-on-Don, Russia.
Faculty of Computer Science, HSE University, 20 Myasnitskaya Str., 101000 Moscow, Russia.
Cancers (Basel). 2024 Mar 8;16(6):1100. doi: 10.3390/cancers16061100.
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（人工智能的一部分）在各类肿瘤学诊断和治疗中的作用正在稳步增强。预计人工智能在肿瘤学中的应用将加快诊断和治疗规划流程。本综述描述了机器学习在肿瘤学中的最新应用，包括医学图像分析、治疗规划、患者生存预后以及即时护理点的药物合成。对于快速发展的癌症形式，医学图像的快速可靠分析至关重要。引入机器学习来分析不断增长的大量数据，使得改善规定治疗的质量和患者护理成为可能。因此，机器学习有望成为医学专家的一项关键技术。与传统分期算法相比，机器学习模型已经改善了对患者的预后预测。在即时护理点直接合成必要的医用物质（小分子混合物）也可能会从机器学习的应用中受益匪浅。我们进一步综述了现代肿瘤学中基于人工智能技术应用的主要趋势。本综述展示了使用机器学习工具在癌症研究以及医学其他领域取得进展的未来前景。尽管在医学实践中对使用现代计算机技术的兴趣日益浓厚，但仍存在一些未解决的伦理和法律问题。在本综述中，我们还讨论了其中最相关的问题。
