Artificial Intelligence in Clinical Laboratories: A Bibliometric Analysis of Research Trends and Applications
DOI:
https://doi.org/10.37287/ijghr.v8i4.1861Keywords:
artificial intelligence, bibliometric analysis, clinical laboratoryAbstract
The progress of artificial intelligence (AI) has rapidly increased its use in clinical laboratories, making it easier to diagnose patients and run the laboratories more efficiently. Nonetheless, difficulties remain regarding the ethical aspects, uniformity, and reliability of algorithmic decision-making. This study seeks to delineate the evolution of AI applications in clinical laboratories over the preceding five years through a bibliometric analysis. We obtained original articles from the Scopus database published between 2021 and 2025 in the fields of medicine, computer science, and health professions. A systematic identification and screening process resulted in 1,891 documents, followed by filtering, duplicate removal, and eligibility assessment, giving in the inclusion of 994 studies in the final analysis. We conducted a bibliometric analysis to examine publication trends, changes in themes over time, co-occurrence of keywords, networks of international collaboration, and highly cited core studies. The results show that research output is growing quickly, and the focus is shifting from developing new methods to putting them into practice in the real world. Keyword mapping indicates that automation, diagnostic accuracy, and decision-support systems are becoming increasingly important. International collaboration is growing, but it is still unevenly distributed across regions. In conclusion, this study offers a thorough examination of the evolving research landscape of AI in clinical laboratories and underscores the need for enhanced global collaboration and the ethical, clinically reliable, and sustainable incorporation of AI in clinical laboratories.
References
Abbas, Q., Jeong, W., & Lee, S. W. (2025). Explainable AI in Clinical Decision Support Systems: A Meta-Analysis of Methods, Applications, and Usability Challenges. Healthcare (Basel, Switzerland), 13(17). https://doi.org/10.3390/healthcare13172154
Ali, O., Abdelbaki, W., Shrestha, A., Elbasi, E., Alryalat, M. A. A., & Dwivedi, Y. K. (2023). A systematic literature review of artificial intelligence in the healthcare sector: Benefits, challenges, methodologies, and functionalities. Journal of Innovation & Knowledge, 8(1). https://doi.org/10.1016/j.jik.2023.100333
AlShebli, B., Memon, S. A., Evans, J. A., & Rahwan, T. (2024). China and the U.S. produce more impactful AI research when collaborating together. Scientific reports, 14(1), 28576. https://doi.org/10.1038/s41598-024-79863-5
Aradhya, S., Facio, F. M., Metz, H., Manders, T., Colavin, A., Kobayashi, Y., Nykamp, K., Johnson, B., & Nussbaum, R. L. (2023). Applications of artificial intelligence in clinical laboratory genomics. American journal of medical genetics. Part C, Seminars in medical genetics, 193(3), e32057. https://doi.org/10.1002/ajmg.c.32057
Ashraf, A. A., Rai, S., Alva, S., Alva, P. D., & Naresh, S. (2025). Revolutionizing clinical laboratories: The impact of artificial intelligence in diagnostics and patient care. Diagnostic Microbiology and Infectious Disease, 111(4), 116728. https://doi.org/https://doi.org/10.1016/j.diagmicrobio.2025.116728
Berkhout, W. E. M., van Wijngaarden, J. J., Workum, J. D., van de Sande, D., Hilling, D. E., Jung, C., Meyfroidt, G., Gommers, D., Buijsman, S. N. R., & van Genderen, M. E. (2025). Operationalization of Artificial Intelligence Applications in the Intensive Care Unit: A Systematic Review. JAMA Netw Open, 8(7), e2522866. https://doi.org/10.1001/jamanetworkopen.2025.22866
Ehrenfeld, J. M., & Woeltje, K. F. (2024). The Challenges of Establishing Assurance Labs for Health Artificial Intelligence (AI). Journal of Medical Systems : The Home of Clinical Informatics Research, 48(1). https://doi.org/10.1007/s10916-024-02127-2
Elechi, U., Orobator, E., Udoh, K., Ngozi, E., Uzoma, C., Forson, K., Akanbi, O., & Tarawallie, M. (2025). Artificial Intelligence in Healthcare: A Narrative Review of Recent Clinical Applications, Implementation Strategies, and Challenges. Journal of Healthcare Leadership, 17, 863-876. https://doi.org/10.2147/JHL.S553748
Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., & Dean, J. (2019). A guide to deep learning in healthcare. Nature Medicine, 25(1), 24-29. https://doi.org/10.1038/s41591-018-0316-z
Giesriegl, F., Mrazek, C., & Cadamuro, J. (2025). How laboratory medicine will change in the near future: integrating artificial intelligence, automation, and human expertise in the era of Industry 5.0. Journal of Laboratory and Precision Medicine, 10. https://jlpm.amegroups.org/article/view/10311
Hajkowicz, S., Sanderson, C., Karimi, S., Bratanova, A., & Naughtin, C. (2023). Artificial intelligence adoption in the physical sciences, natural sciences, life sciences, social sciences and the arts and humanities: A bibliometric analysis of research publications from 1960-2021. Technology in Society, 74. https://doi.org/10.1016/j.techsoc.2023.102260
Hou, H., Zhang, R., & Li, J. (2024). Artificial intelligence in the clinical laboratory. Clinica Chimica Acta, 559. https://doi.org/10.1016/j.cca.2024.119724
Kelly, C. J., Karthikesalingam, A., Suleyman, M., Corrado, G., & King, D. (2019). Key challenges for delivering clinical impact with artificial intelligence. BMC medicine, 17(1), 195. https://doi.org/10.1186/s12916-019-1426-2
Krittanawong, C., Zhang, H., Wang, Z., Aydar, M., & Kitai, T. (2017). Artificial Intelligence in Precision Cardiovascular Medicine. Journal of the American College of Cardiology, 69(21), 2657-2664. https://doi.org/10.1016/j.jacc.2017.03.571
Lukman, S., & Atoe, K. (2025). Artificial Intelligence in Laboratory Medicine: A Review of Current Uses and Future Prospects in Diagnostic Imaging, Data Analysis, and Decision-Making Tools. Open Journal of Medical Research (ISSN: 2734-2093), 6, 39-51. https://doi.org/10.52417/ojmr.v6i2.975
Md Habibur, R., Md Abdur Rahman, B., Asim, D., Md Abu Bakar, S., Md Mostafizur, R., Md Mehedi Hasan, R., & Moushumi Afroza, M. (2025). The Future of AI in Laboratory Medicine: Advancing Diagnostics, Personalization, and Healthcare Innovation. Journal of Primeasia, 6(1). https://doi.org/10.25163/primeasia.6110151
Ni, R., Qu, Y., & Wang, J. (2026). Bridging or Widening the Gap? The Role of AI in Shaping Global Research Performance. Journal of Chinese Political Science, 1-39. https://doi.org/10.1007/s11366-026-09931-2
Rajkomar, A., Oren, E., Chen, K., Dai, A. M., Hajaj, N., Hardt, M., Liu, P. J., Liu, X., Marcus, J., Sun, M., Sundberg, P., Yee, H., Zhang, K., Zhang, Y., Flores, G., Duggan, G. E., Irvine, J., Le, Q., Litsch, K.,…Dean, J. (2018). Scalable and accurate deep learning with electronic health records. NPJ digital medicine, 1, 18. https://doi.org/10.1038/s41746-018-0029-1
Sadeghi, Z., Alizadehsani, R., Cifci, M. A., Kausar, S., Rehman, R., Mahanta, P., Bora, P. K., Almasri, A., Alkhawaldeh, R. S., Hussain, S., Alatas, B., Shoeibi, A., Moosaei, H., Hladík, M., Nahavandi, S., & Pardalos, P. M. (2024). A review of Explainable Artificial Intelligence in healthcare. Computers and Electrical Engineering, 118. https://doi.org/10.1016/j.compeleceng.2024.109370
Sendak, M. P., Ratliff, W., Sarro, D., Alderton, E., Futoma, J., Gao, M., Nichols, M., Revoir, M., Yashar, F., Miller, C., Kester, K., Sandhu, S., Corey, K., Brajer, N., Tan, C., Lin, A., Brown, T., Engelbosch, S., Anstrom, K.,…O'Brien, C. (2020). Real-World Integration of a Sepsis Deep Learning Technology Into Routine Clinical Care: Implementation Study. JMIR Medical Informatics, 8(7), e15182. https://doi.org/10.2196/15182
Senthil, R., Anand, T., Somala, C. S., & Saravanan, K. M. (2024). Bibliometric analysis of artificial intelligence in healthcare research: Trends and future directions. Future Healthcare Journal, 11(3). https://doi.org/10.1016/j.fhj.2024.100182
Topol, E. J. (2019). High-performance medicine: the convergence of human and artificial intelligence. Nature Medicine, 25(1), 44-56. https://doi.org/10.1038/s41591-018-0300-7
van Diest, P. J., Flach, R. N., van Dooijeweert, C., Makineli, S., Breimer, G. E., Stathonikos, N., Pham, P., Nguyen, T. Q., & Veta, M. (2024). Pros and cons of artificial intelligence implementation in diagnostic pathology. Histopathology, 84(6), 924-934. https://doi.org/10.1111/his.15153
Wang, L., Zhang, Y., Wang, D., Tong, X., Liu, T., Zhang, S., Huang, J., Zhang, L., Chen, L., Fan, H., & Clarke, M. (2021). Artificial Intelligence for COVID-19: A Systematic Review. Front Med (Lausanne), 8, 704256. https://doi.org/10.3389/fmed.2021.704256
Xie, H., Jia, Y., & Liu, S. (2024). Integration of artificial intelligence in clinical laboratory medicine: Advancements and challenges. Interdisciplinary Medicine, 2(3). https://doi.org/10.1002/INMD.20230056
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Indonesian Journal of Global Health Research
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
