Bibliometric analysis of the internet of medical things (IoMT)

The Internet of Medical Things (IoMT) is a subset of IoT technology adapted for the healthcare domain, enabling remote diagnosis, treatment, and patient monitoring. Through wearable and implantable medical sensors, network devices, and cloud systems, patient data is continuously collected, analyzed, and transmitted to healthcare professionals. However, most academic studies in the IoMT field focus on technical aspects and exhibit issues related to methodological diversity and up-to-dateness in terms of bibliometric analyses. This study offers a comprehensive and up-to-date analysis aiming to identify research trends, academic collaborations, and gaps in the IoMT literature. In this context, bibliometric performance analysis, bibliographic coupling, keyword co-occurrence, and co-authorship analysis methods were applied to articles published in the Web of Science database over the last five years. Visualization and data analysis conducted using VOSviewer and Biblioshiny software revealed research trends in the IoMT field and produced findings that can contribute to future studies. According to the findings, the scientific output in the IoMT domain showed an increasing trend from 2020 to 2023, reaching its peak in 2023, but experienced a 27.7% decline in 2024, indicating a downward trend. The most influential journal was found to be IEEE Access, the institution with the highest number of publications was King Saud University, and the country with the most citations was China. The strongest collaboration occurred between Saudi Arabia and Pakistan, while the highest levels of productivity and collaboration were observed among Asian countries. According to the co-authorship analysis, the most prolific author was Taher M. Ghazal, while the most cited author was Bashir Ali Kashif. The study found moderate compliance with Lotka's Law, whereas Bradford's Law was deemed inapplicable. In the publications with the highest citations within the clusters identified through bibliographic coupling analysis, studies related to COVID-19 were predominant, while machine learning, blockchain, and authentication emerged as other thematic areas. As an important contribution to the literature, the study reveals that the field of IoMT is dominated by technical focus, whereas sociotechnical dimensions are neglected, and emphasizes the need for social science-focused, interdisciplinary studies.