Large Language Models in Healthcare: A Bibliometric Analysis and Examination of Research Trends
Gülcan Gencer,1 Kerem Gencer2 1Department of Biostatistics and Medical Informatics, Afyonkarahisar Health Sciences University, Faculty of Medicine, Afyonkarahisar, Turkey; 2Department of Computer Engineering, Afyon Kocatepe University, Faculty of Engineering, Afyonkarahisar, TurkeyCorrespondence: Gü...
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Dove Medical Press
2025-01-01
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| Series: | Journal of Multidisciplinary Healthcare |
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| Online Access: | https://www.dovepress.com/large-language-models-in-healthcare-a-bibliometric-analysis-and-examin-peer-reviewed-fulltext-article-JMDH |
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| author | Gencer G Gencer K |
| author_facet | Gencer G Gencer K |
| author_sort | Gencer G |
| collection | DOAJ |
| description | Gülcan Gencer,1 Kerem Gencer2 1Department of Biostatistics and Medical Informatics, Afyonkarahisar Health Sciences University, Faculty of Medicine, Afyonkarahisar, Turkey; 2Department of Computer Engineering, Afyon Kocatepe University, Faculty of Engineering, Afyonkarahisar, TurkeyCorrespondence: Gülcan Gencer, Department of Biostatistics and Medical Informatics, Afyonkarahisar Health Sciences University, Faculty of Medicine, Afyonkarahisar, Turkey, Email gencergulcan@gmail.comBackground: The integration of large language models (LLMs) in healthcare has generated significant interest due to their potential to improve diagnostic accuracy, personalization of treatment, and patient care efficiency.Objective: This study aims to conduct a comprehensive bibliometric analysis to identify current research trends, main themes and future directions regarding applications in the healthcare sector.Methods: A systematic scan of publications until 08.05.2024 was carried out from an important database such as Web of Science.Using bibliometric tools such as VOSviewer and CiteSpace, we analyzed data covering publication counts, citation analysis, co-authorship, co- occurrence of keywords and thematic development to map the intellectual landscape and collaborative networks in this field.Results: The analysis included more than 500 articles published between 2021 and 2024. The United States, Germany and the United Kingdom were the top contributors to this field. The study highlights that neural network applications in diagnostic imaging, natural language processing for clinical documentation, and patient data in the field of general internal medicine, radiology, medical informatics, health care services, surgery, oncology, ophthalmology, neurology, orthopedics and psychiatry have seen significant growth in publications over the past two years. Keyword trend analysis revealed emerging sub-themes such as clinical research, artificial intelligence, ChatGPT, education, natural language processing, clinical management, virtual reality, chatbot, indicating a shift towards addressing the broader implications of LLM application in healthcare.Conclusion: The use of LLM in healthcare is an expanding field with significant academic and clinical interest. This bibliometric analysis not only maps the current state of the research, but also identifies important areas that require further research and development. Continued advances in this field are expected to significantly impact future healthcare applications, with a focus on increasing the accuracy and personalization of patient care through advanced data analytics.Keywords: large language models, chatbot, healthcare, artificial intelligence, clinical applications, diagnosis, treatment recommendations |
| format | Article |
| id | doaj-art-8fb3455d6da84a00930af53814531954 |
| institution | Kabale University |
| issn | 1178-2390 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Dove Medical Press |
| record_format | Article |
| series | Journal of Multidisciplinary Healthcare |
| spelling | doaj-art-8fb3455d6da84a00930af538145319542025-01-16T16:17:13ZengDove Medical PressJournal of Multidisciplinary Healthcare1178-23902025-01-01Volume 1822323899327Large Language Models in Healthcare: A Bibliometric Analysis and Examination of Research TrendsGencer GGencer KGülcan Gencer,1 Kerem Gencer2 1Department of Biostatistics and Medical Informatics, Afyonkarahisar Health Sciences University, Faculty of Medicine, Afyonkarahisar, Turkey; 2Department of Computer Engineering, Afyon Kocatepe University, Faculty of Engineering, Afyonkarahisar, TurkeyCorrespondence: Gülcan Gencer, Department of Biostatistics and Medical Informatics, Afyonkarahisar Health Sciences University, Faculty of Medicine, Afyonkarahisar, Turkey, Email gencergulcan@gmail.comBackground: The integration of large language models (LLMs) in healthcare has generated significant interest due to their potential to improve diagnostic accuracy, personalization of treatment, and patient care efficiency.Objective: This study aims to conduct a comprehensive bibliometric analysis to identify current research trends, main themes and future directions regarding applications in the healthcare sector.Methods: A systematic scan of publications until 08.05.2024 was carried out from an important database such as Web of Science.Using bibliometric tools such as VOSviewer and CiteSpace, we analyzed data covering publication counts, citation analysis, co-authorship, co- occurrence of keywords and thematic development to map the intellectual landscape and collaborative networks in this field.Results: The analysis included more than 500 articles published between 2021 and 2024. The United States, Germany and the United Kingdom were the top contributors to this field. The study highlights that neural network applications in diagnostic imaging, natural language processing for clinical documentation, and patient data in the field of general internal medicine, radiology, medical informatics, health care services, surgery, oncology, ophthalmology, neurology, orthopedics and psychiatry have seen significant growth in publications over the past two years. Keyword trend analysis revealed emerging sub-themes such as clinical research, artificial intelligence, ChatGPT, education, natural language processing, clinical management, virtual reality, chatbot, indicating a shift towards addressing the broader implications of LLM application in healthcare.Conclusion: The use of LLM in healthcare is an expanding field with significant academic and clinical interest. This bibliometric analysis not only maps the current state of the research, but also identifies important areas that require further research and development. Continued advances in this field are expected to significantly impact future healthcare applications, with a focus on increasing the accuracy and personalization of patient care through advanced data analytics.Keywords: large language models, chatbot, healthcare, artificial intelligence, clinical applications, diagnosis, treatment recommendationshttps://www.dovepress.com/large-language-models-in-healthcare-a-bibliometric-analysis-and-examin-peer-reviewed-fulltext-article-JMDHlarge language modelschatbothealthcareartificial intelligenceclinical applicationsdiagnosistreatment recommendations. |
| spellingShingle | Gencer G Gencer K Large Language Models in Healthcare: A Bibliometric Analysis and Examination of Research Trends Journal of Multidisciplinary Healthcare large language models chatbot healthcare artificial intelligence clinical applications diagnosis treatment recommendations. |
| title | Large Language Models in Healthcare: A Bibliometric Analysis and Examination of Research Trends |
| title_full | Large Language Models in Healthcare: A Bibliometric Analysis and Examination of Research Trends |
| title_fullStr | Large Language Models in Healthcare: A Bibliometric Analysis and Examination of Research Trends |
| title_full_unstemmed | Large Language Models in Healthcare: A Bibliometric Analysis and Examination of Research Trends |
| title_short | Large Language Models in Healthcare: A Bibliometric Analysis and Examination of Research Trends |
| title_sort | large language models in healthcare a bibliometric analysis and examination of research trends |
| topic | large language models chatbot healthcare artificial intelligence clinical applications diagnosis treatment recommendations. |
| url | https://www.dovepress.com/large-language-models-in-healthcare-a-bibliometric-analysis-and-examin-peer-reviewed-fulltext-article-JMDH |
| work_keys_str_mv | AT gencerg largelanguagemodelsinhealthcareabibliometricanalysisandexaminationofresearchtrends AT gencerk largelanguagemodelsinhealthcareabibliometricanalysisandexaminationofresearchtrends |