The rapid growth of healthcare data in recent years calls for more advanced and efficient analytic techniques. Artificial intelligence facilitates finding insightful patterns in massive high-dimensional data. Considering the latest movements towards using machine learning and deep learning techniques in the medical domain, in this study, we focused on the publications in which researchers employed artificial intelligence techniques for cancer diagnosis and treatment. Using dynamic topic modeling and natural language processing techniques, we analyzed the contents and trends of more than 12,000 scientific publications within the period of 2000 to 2018, extracted from two different sources, i.e., Elsevier’s Scopus and PubMed. While drawing the landscape of cancer research, our results also shed light on the evolution of artificial intelligence techniques and algorithms used for cancer diagnosis and treatment. Our findings confirm that modern computer science algorithms are being widely applied to extract patterns from large-scale medical images to cure different types of cancer with a special focus on deep learning techniques in recent years.

中文翻译：

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使用动态主题建模发现人工智能在癌症研究中的演变

近年来医疗保健数据的快速增长需要更先进和更有效的分析技术。人工智能有助于在海量高维数据中找到富有洞察力的模式。考虑到在医学领域使用机器学习和深度学习技术的最新趋势，在本研究中，我们重点关注研究人员使用人工智能技术进行癌症诊断和治疗的出版物。利用动态主题建模和自然语言处理技术，我们分析了 2000 年至 2018 年期间从 Elsevier 的 Scopus 和 PubMed 两个不同来源提取的 12,000 多篇科学出版物的内容和趋势。在描绘癌症研究的风景时，我们的研究结果还揭示了用于癌症诊断和治疗的人工智能技术和算法的演变。我们的研究结果证实，现代计算机科学算法正被广泛应用于从大规模医学图像中提取模式，以治愈不同类型的癌症，近年来特别关注深度学习技术。