The Role of AI in Nursing Education and Practice: Umbrella Review

19 indexed citations
published 2025

Countries where authors are citing The Role of AI in Nursing Education and Practice: Umbrella Review

Specialization
Citations

This map shows the geographic impact of The Role of AI in Nursing Education and Practice: Umbrella Review. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by The Role of AI in Nursing Education and Practice: Umbrella Review with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites The Role of AI in Nursing Education and Practice: Umbrella Review more than expected).

Fields of papers citing The Role of AI in Nursing Education and Practice: Umbrella Review

Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of The Role of AI in Nursing Education and Practice: Umbrella Review. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the The Role of AI in Nursing Education and Practice: Umbrella Review.

About The Role of AI in Nursing Education and Practice: Umbrella Review

This paper, published in 2025, received 19 indexed citations . Written by Rabie Adel El Arab and Fuad H. Abuadas covering the research area of Health Informatics, Health Information Management and Radiology, Nuclear Medicine and Imaging. It is primarily cited by scholars working on Health Informatics (14 citations), Artificial Intelligence (4 citations) and Public Health, Environmental and Occupational Health (4 citations). Published in Journal of Medical Internet Research.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

This paper is also available at doi.org/10.2196/69881.

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