National Medical and Surgical Center Named after N. I. Pirogov, Ministry of Healthcare of the Russian Federation, 105203 Moscow, Russia.
Baltic Center for Neurotechnology and Artificial Intelligence, Immanuel Kant Baltic Federal University, 236041 Kaliningrad, Russia.
Int J Environ Res Public Health. 2023 Mar 30;20(7):5335. doi: 10.3390/ijerph20075335.
Artificial intelligence (AI) has revolutionized numerous industries, including medicine. In recent years, the integration of AI into medical practices has shown great promise in enhancing the accuracy and efficiency of diagnosing diseases, predicting patient outcomes, and personalizing treatment plans. This paper aims at the exploration of the AI-based medicine research using network approach and analysis of existing trends based on PubMed. Our findings are based on the results of PubMed search queries and analysis of the number of papers obtained by the different search queries. Our goal is to explore how are the AI-based methods used in healthcare research, which approaches and techniques are the most popular, and to discuss the potential reasoning behind the obtained results. Using analysis of the co-occurrence network constructed using VOSviewer software, we detected the main clusters of interest in AI-based healthcare research. Then, we proceeded with the thorough analysis of publication activity in various categories of medical AI research, including research on different AI-based methods applied to different types of medical data. We analyzed the results of query processing in the PubMed database over the past 5 years obtained via a specifically designed strategy for generating search queries based on the thorough selection of keywords from different categories of interest. We provide a comprehensive analysis of existing applications of AI-based methods to medical data of different modalities, including the context of various medical fields and specific diseases that carry the greatest danger to the human population.
人工智能（AI）已经彻底改变了许多行业，包括医学。近年来，将 AI 融入医学实践在提高疾病诊断的准确性和效率、预测患者预后和个性化治疗计划方面显示出巨大的潜力。本文旨在通过网络方法探索基于 AI 的医学研究，并基于 PubMed 分析现有趋势。我们的发现基于 PubMed 搜索查询的结果和不同搜索查询获得的论文数量的分析。我们的目标是探索 AI 方法在医疗保健研究中的应用方式、最受欢迎的方法和技术，以及讨论获得结果的潜在原因。我们使用 VOSviewer 软件构建的共现网络分析，检测到基于 AI 的医疗保健研究中的主要兴趣集群。然后，我们对不同类型的医疗 AI 研究中的各个领域的出版物活动进行了深入分析，包括对不同类型的医疗数据应用的不同 AI 方法的研究。我们分析了过去 5 年中通过基于从不同类别中精心选择的关键字生成搜索查询的特定策略在 PubMed 数据库中查询处理的结果。我们对基于 AI 的方法在不同模式的医疗数据中的现有应用进行了全面分析，包括各种医学领域的背景和对人类构成最大危险的特定疾病。
