A Bibliometric Analysis of Research on Nursing Diagnosis and Decision Support Systems with Artificial Intelligence (2009-2023)
6 affiliations
- 1Professor, Department of Community Health Nursing, College of Nursing, Taibah University, Al-Madinah, Saudi Arabia.
- 2Associate Professor, Department of Biomedical Research, Medical Research Centre, Jazan University, Jazan, Saudi Arabia.
- 3Assistant Professor, Department of Nursing, College of Applied Medical Science, University of Bisha, Bisha, Saudi Arabia.
- 4Assistant Professor, Department of Nursing, College of Applied Medical Science, University of Hafr Al Batin, Hafr Al Batin, Saudi Arabia.
- 5Assistant Professor, Department of Nursing, Vision Medical Colleges, Jeddah, Saudi Arabia.
- 6Assistant Professor, Department of Nursing, College of Nursing and Health Science, Jazan University, Jazan, Saudi Arabia.
Abstract
Introduction: The integration of Artificial Intelligence (AI) into Nursing Diagnosis and Decision Support Systems (NDDSAI), has been explored in numerous studies. Objective: This study aims to investigate the contributors, publication trends, citation rate, themes, and topics of studies related to NDDSAI research. Materials and Methods: This is a bibliometric study. NDDSAI-related publications from 2009 to 2023 were searched in the Scopus database using MeSH terms and Boolean operators. Bibliographic data were exported in the relevant formats. For thematic mapping, we used VOSviewer software and the bibliometrix R package. The study covered publication trend assessment, contributors, local and global citation rates, thematic evolution, co-word analysis, and co-citation analysis. Results: Annual scientific production on NDDSAI (n=254) fluctuated since 2009. From 2009 onward, the annual number of publications was inconsistent. The average annual growth rate was 4.67 articles per year. The mean age of the publications was 8.39±1.3 years. The USA led in production, citations, and co-authorships. Major research themes included “machine learning,” “nursing process,” and “nursing diagnosis.” Emerging topics included “artificial neural network” and “advance care planning.” The clusters “decision support” and “diagnosis” remained stable across both periods analyzed. Conclusion: This study revealed the trends of NDDSAI research and highlighted AI’s vital role in nursing diagnosis and decision support. By identifying key research areas, contributors, and topics, this study offers a comprehensive overview of NDDSAI’s current status and future directions, emphasizing enduring and evolving research themes.
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BibTeX
@article{fadlalmola2026bibliometric,
title = {{A Bibliometric Analysis of Research on Nursing Diagnosis and Decision Support Systems with Artificial Intelligence (2009-2023)}},
author = {Hammad Ali Fadlalmola and Siddig Ibrahim Abdelwahab and Amal Mohamed Elhusein and Manal Mohamed Elhassan Taha and Mukhlid Alshammari and Khalida Hussain Sharaf and Soad Mohamed Alnasri},
journal = {Journal of Holistic Nursing and Midwifery},
year = {2026},
month = sep,
note = {CC BY-NC},
}
RIS
TY - JOUR
TI - A Bibliometric Analysis of Research on Nursing Diagnosis and Decision Support Systems with Artificial Intelligence (2009-2023)
AU - Hammad Ali Fadlalmola
AU - Siddig Ibrahim Abdelwahab
AU - Amal Mohamed Elhusein
AU - Manal Mohamed Elhassan Taha
AU - Mukhlid Alshammari
AU - Khalida Hussain Sharaf
AU - Soad Mohamed Alnasri
PY - 2026
DA - 2026/09/01
JO - Journal of Holistic Nursing and Midwifery
AB - Introduction: The integration of Artificial Intelligence (AI) into Nursing Diagnosis and Decision Support Systems (NDDSAI), has been explored in numerous studies. Objective: This study aims to investigate the contributors, publication trends, citation rate, themes, and topics of studies related to NDDSAI research. Materials and Methods: This is a bibliometric study. NDDSAI-related publications from 2009 to 2023 were searched in the Scopus database using MeSH terms and Boolean operators. Bibliographic data were exported in the relevant formats. For thematic mapping, we used VOSviewer software and the bibliometrix R package. The study covered publication trend assessment, contributors, local and global citation rates, thematic evolution, co-word analysis, and co-citation analysis. Results: Annual scientific production on NDDSAI (n=254) fluctuated since 2009. From 2009 onward, the annual number of publications was inconsistent. The average annual growth rate was 4.67 articles per year. The mean age of the publications was 8.39±1.3 years. The USA led in production, citations, and co-authorships. Major research themes included “machine learning,” “nursing process,” and “nursing diagnosis.” Emerging topics included “artificial neural network” and “advance care planning.” The clusters “decision support” and “diagnosis” remained stable across both periods analyzed. Conclusion: This study revealed the trends of NDDSAI research and highlighted AI’s vital role in nursing diagnosis and decision support. By identifying key research areas, contributors, and topics, this study offers a comprehensive overview of NDDSAI’s current status and future directions, emphasizing enduring and evolving research themes.
SN - 2588-3712
SN - 2588-3720
ER -