Mapping Artificial Intelligence in Medical Diagnosis in India: A Bibliometric Analysis

Ziaur Rahman; Akhtar  Hussain; Md. Safiqur  Rahaman; Khadeeja M N  Ansari

Authors

Ziaur Rahman Associate Professor & Head, Department of Library and Information Science, Rabindra Bharati University, Kolkata https://orcid.org/0000-0002-3393-8690
Akhtar Hussain Librarian, , Hamdard Institute of Medical Sciences & Research (HIMSR), Jamia Hamdard
Md. Safiqur Rahaman Librarian, Deanship of Library Affairs, King Fahd University of Petroleum and Minerals, Dhahran, https://orcid.org/0000-0003-1367-2618
Khadeeja M N Ansari Research Scholar, Department of Library and Information Science Banasthali Vidyapith, https://orcid.org/0000-0002-1451-3822

Keywords:

Bibliometric, Artificial Intelligence, Medical diagnosis, Co-Citation, Trend analysis.

Abstract

The integration of Artificial Intelligence (AI) into medical diagnosis holds immense potential for revolutionizing healthcare practices. This bibliometric study explores the landscape of AI research within the field of medical science, focusing specifically on India's trajectory. This study employs bibliometric analysis to examine the landscape of Artificial Intelligence (AI) in medical science research, spanning from 2004 to 2023. A total of 2077 research papers authored by 9,982 individuals from 627 sources were analyzed, revealing a collaborative environment with an average of 7.25 co-authors per document, over half of which involved international collaboration. Fluctuations in researchers' productivity and citation impact were illustrated through publication and citation trends. Highly productive authors, top journals, and contributing organizations were identified, providing insights into their publication records and scholarly prominence. Document categorization highlighted the varying impacts of publication types. Co-citation analysis elucidated research themes such as stroke risk stratification, diabetic retinopathy, and spectral data analysis. Key author keywords and their co-occurrence patterns revealed prevalent themes such as machine learning and COVID-19. Moreover, analysis of citation bursts unveiled topics experiencing heightened scholarly attention over time. The landscape of international collaboration demonstrated India's significant engagement, particularly with countries like the United States, the United Kingdom, and Italy, indicating widespread global participation in AI-driven medical research. This comprehensive examination offers valuable insights into the evolution, trends, and collaborative dynamics of AI research in medical science, facilitating further understanding and advancement in this critical domain.

References

Agac, G., Sevim, F., Celik, O., Bostan, S., Erdem, R., & Yalcin, Y. I. (2023). Research hotspots, trends and opportunities on the metaverse in health education: a bibliometric analysis. Library Hi Tech.

Al Kuwaiti, A., Nazer, K., Al-Reedy, A., Al-Shehri, S., Al-Muhanna, A., Subbarayalu, A. V., Al Muhanna, D., & Al-Muhanna, F. A. (2023). A Review of the Role of Artificial Intelligence in Healthcare. Journal of Personalized Medicine, 13(6), 951.

Bajpai, N., & Wadhwa, M. (2021). Artificial Intelligence and Healthcare in India. ICT India Working Paper.

Bohr, A., & Memarzadeh, K. (2020). The rise of artificial intelligence in healthcare applications. In Artificial Intelligence in healthcare (pp. 25–60). Elsevier.

Bornmann, L., & Leydesdorff, L. (2014). Scientometrics in a changing research landscape: bibliometrics has become an integral part of research quality evaluation and has been changing the practice of research. EMBO Reports, 15(12), 1228–1232.

Cobelli, N., & Blasioli, E. (2023). To be or not to be digital? A bibliometric analysis of adoption of eHealth services. The TQM Journal, 35(9), 299–331.

Gao, F., Jia, X., Zhao, Z., Chen, C. C., Xu, F., Geng, Z., & Song, X. (2021). Bibliometric analysis on tendency and topics of artificial intelligence over last decade. Microsystem Technologies, 27(4), 1545–1557. https://doi.org/10.1007/s00542-019-04426-y

Gurmessa, D. K., & Jimma, W. (2023). A comprehensive evaluation of explainable Artificial Intelligence techniques in stroke diagnosis: A systematic review. Cogent Engineering, 10(2), 2273088.

Hussain,A.; Azad,M.A.;Ahmad,S.;Sahay,A.;Fatima, N. (2021). Mapping of Research Output on Learning Disabilities : A Bibliometric Study. European Journal of Molecular & Clinical Medicine, 08(03), 165–185.

Lareyre, F., Lê, C. D., Ballaith, A., Adam, C., Carrier, M., Amrani, S., Caradu, C., & Raffort, J. (2022). Applications of artificial intelligence in non-cardiac vascular diseases: a bibliographic analysis. Angiology, 73(7), 606–614.

Li, C., Wang, L., Perka, C., & Trampuz, A. (2021). Clinical application of robotic orthopedic surgery: a bibliometric study. BMC Musculoskeletal Disorders, 22(1). https://doi.org/10.1186/s12891-021-04714-7

Li, J. (2022). The Innovation of Library Service by Artificial Intelligence Robot. 2022 3rd International Conference on Artificial Intelligence, Robotics and Control (AIRC), 57–60.

Loan, F. A., Bashir, B., & Nasreen, N. (2021). Applied artificial intelligence: A bibliometric study of an International Journal. Collnet Journal of Scientometrics and Information Management, 15(1), 27–45.

Ma, T., Wu, Q., Jiang, L., Zeng, X., Wang, Y., Yuan, Y., Wang, B., & Zhang, T. (2023). Artificial intelligence and machine (Deep) learning in otorhinolaryngology: A bibliometric analysis based on VOSviewer and citeSpace. Ear, Nose & Throat Journal, 01455613231185074.

Mishra, S., Takke, A., Auti, S., Suryavanshi, S., & Oza, M. (2017). Role of artificial intelligence in health care. BioChemistry: An Indian Journal, 11(5), 1–14.

Musa, I. H., Afolabi, L. O., Zamit, I., Musa, T. H., Musa, H. H., Tassang, A., Akintunde, T. Y., & Li, W. (2022). Artificial intelligence and machine learning in cancer research: a systematic and thematic analysis of the top 100 cited articles indexed in Scopus database. Cancer Control, 29, 10732748221095946.

Penteado, B. E., Fornazin, M., & Castro, L. (2021). The evolution of artificial intelligence in medical informatics: A bibliometric analysis. EPIA Conference on Artificial Intelligence, 121–133.

Rajkomar, A., Dean, J., & Kohane, I. (2019). Machine learning in medicine. New England Journal of Medicine, 380(14), 1347–1358.

Saheb, T., Saheb, T., & Carpenter, D. O. (2021). Mapping research strands of ethics of artificial intelligence in healthcare: a bibliometric and content analysis. Computers in Biology and Medicine, 135, 104660.

Sweileh, W. (2023). Analysis and mapping of scientific literature on virtual and augmented reality technologies used in the context of mental health disorders (1980–2021). The Journal of Mental Health Training, Education and Practice.

Tchuente Foguem, G., & Teguede Keleko, A. (2023). Artificial intelligence applied in pulmonary hypertension: a bibliometric analysis. AI and Ethics, 1–31.

Topol, E. J. (2019). High-performance medicine: the convergence of human and artificial intelligence. Nature Medicine, 25(1), 44–56.

Zamit, I., Musa, I. H., Jiang, L., Yanjie, W., & Tang, J. (2022). Trends and features of autism spectrum disorder research using artificial intelligence techniques: a bibliometric approach. Current Psychology. https://doi.org/10.1007/s12144-022-03977-0

Zhang, F., Wang, L., Zhao, J., & Zhang, X. (2023). Medical applications of generative adversarial network: a visualization analysis. Acta Radiologica, 64(10), 2757–2767.

Mapping Artificial Intelligence in Medical Diagnosis in India: A Bibliometric Analysis

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