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.

近年来，医疗保健数据的快速增长需要更先进、更高效的分析技术。人工智能有助于在海量高维数据中找到有洞察力的模式。考虑到在医学领域使用机器学习和深度学习技术的最新动向，在本研究中，我们重点关注研究人员采用人工智能技术进行癌症诊断和治疗的出版物。我们使用动态主题建模和自然语言处理技术，分析了 2000 年至 2018 年期间从两个不同来源（Elsevier's Scopus 和 PubMed）中提取的 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，我们的结果还阐明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，近年来，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疗不同类型的癌症，尤其是深度学习技术。