Bibliometric and Content Analysis of Articles on Artificial Intelligence in Healthcare
Journal Title: Journal of International Health Sciences and Management - Year 2024, Vol 10, Issue 20
Abstract
The use of artificial intelligence in the healthcare sector is becoming widespread for reasons such as analyzing digital patient data, including it in decision-making processes, improving the quality of healthcare services, and providing cost, time, and access advantages. This study aims to evaluate published articles on bibliometric indicators and the use of artificial intelligence in the healthcare sector and examine the content of the most cited articles. Articles about artificial intelligence in the health sector in the Web of Science database were included in the study using the criteria of “keyword, publication year, and publication language”. The research covers 2,680 articles published in English by 14,195 authors from 106 countries in 1084 journals between 2020-2024. 4,671 different keywords were used in the published articles. The country that published the most was “USA”, the journal was “Journal of Medical Internet Research”, the author was “Meng Ji”, and the most cited author was “Weihua Li”. The 55 most cited (≥50) articles focused on themes related to “diagnosis of COVID-19 disease”, “diagnosis of diseases”, “detection and classification of cancerous cells”, “identification of disease risk factors and disease prediction”, “prediction of treatment outcomes”, “prediction of disease course”, “personalized treatment recommendations”, “decision-making processes”, “ethical considerations, risks, and responsibilities”. With the COVID-19 pandemic, it is seen that the number of articles on artificial intelligence in the healthcare sector has increased exponentially. In the research, articles related to artificial intelligence in the health sector were examined, and a framework was created for researchers by revealing the most publishing countries, journals, authors, most cited authors, and keywords that were used the most.
Authors and Affiliations
İbrahim Türkmen, Arif Söyler, Seymur Aliyev, Tarık Semiz
Keywords
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- EP ID EP755061
- DOI https://doi.org/10.48121/jihsam.1533583
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