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. 2023 Mar 30;20(7):5335.
doi: 10.3390/ijerph20075335.

Analysis of Publication Activity and Research Trends in the Field of AI Medical Applications: Network Approach

Oleg E Karpov  1 , Elena N Pitsik  2 , Semen A Kurkin  2 , Vladimir A Maksimenko  2 , Alexander V Gusev  3   4 , Natali N Shusharina  2 , Alexander E Hramov  2
Affiliations

Analysis of Publication Activity and Research Trends in the Field of AI Medical Applications: Network Approach

Oleg E Karpov et al. Int J Environ Res Public Health. .

Abstract

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.

Keywords: artificial intelligence; artificial neural network; deep learning; machine learning; medical area; medical data; supervised learning; unsupervised learning.

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Conflict of interest statement

The authors declare no conflict of interest. The funders and organizations had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

Figures

Figure 1
Figure 1
(A) dynamics of venture capital investment in artificial intelligence systems for medicine and healthcare, according to CB Insights, USD billion. Based on data from ‘State of AI 2021 Report’ [Internet]. Source: https://www.cbinsights.com/research/report/ai-trends-2021/ (accessed on 21 September 2022). (B) dynamics of number of AI in medicine papers by year indexed in the PubMed database.
Figure 2
Figure 2
Query construction scheme. (A)–the construction scheme of queries of the first type. (B)–the construction scheme of queries of the second type.
Figure 3
Figure 3
The co-occurrence network constructed using VOSviewer. The size of the nodes are determined by the weight of the corresponding item that indicate the importance of the item, and the color is determined by the cluster to which the item belongs. The methodology of VOSviewer visualization technique is provided in details in [40].
Figure 4
Figure 4
Selected subnetworks of interest with the largest nodes corresponding to deep learning (A), retrospective studies (B), machine learning (C), electrcardiography (D) and electroencephalography (E).
Figure 5
Figure 5
Results of query research of the PubMed database with the number of papers acquired via the corresponding search queries. (A) the use of machine learning types in combination with different medical data types; (B) medical data in artificial intelligence research in different medical areas; and (C) the use of machine learning methods in different medical area.
Figure 6
Figure 6
The results of the queries with the number of the acquired papers on particular diseases research in combination with different data (A) and machine learning types (B).
See this image and copyright information in PMC

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