Evolution of machine learning applications in medical and healthcare analytics research: A bibliometric analysis

Abstract

• The current status is presented, along with the publication patterns from 1994 to 2023, as well as the topic area categories, which include the general analysis and fundamental features that are provided include the following: total publications (TP), and percentage total publications (%TP) for MDLHC research, several viewpoints on types and research directions, as well as significant indicators at the levels of countries, institutions, and funding organizations. Furthermore, this study presents the varying number of highest publications and citations within the past decade (1994–2023). • Analysing the collaborations at the level of countries, authorship and institutions, the corresponding networks are demonstrated by network visualisation map for co-authorship on MLHC research, network visualisation map for collaborating countries on MLHC research. • The themes of all publications and the top influential journals are aggregated based on the author-keywords analysis aimed to help researchers understand the hotspots and focus. • Ultimately, the study seeks to add impetus to the current discourse surrounding the growth of ML in HC, nurturing a greater comprehension of its transformative prospects as well as challenges that require tackling to harness its complete benefits. • According to all the analyses and visualisation maps, It is also envisaged that the insights garnered from the study will avail academics, politicians, and medical practitioners with critical insights that could stimulate pioneering research initiatives and novel collaborations This bibliometric research explores the global evolution of machine learning applications in medical and healthcare research for 3 decades (1994 to 2023). The study applies data mining techniques to a comprehensive dataset of published articles related to machine learning applications in the medical and healthcare sectors. The data extraction process includes the retrieval of relevant information from the source sources such as journals, books, and conference proceedings. An analysis of the extracted data is then conducted to identify the trends in the machine learning applications in medical and healthcare research. The Results revealed the publications published and indexed in the Scopus and PubMed database over the last 30 years. Bibliometric Analysis revealed that funding played a more significant role in publication productivity compared to collaboration (co-authorships), particularly at the country level. Hotspots analysis revealed three core research themes on MLHC research hence demonstrating the importance of machine learning applications to medical and healthcare research. Further, the study showed that the MLHC research landscape has largely focused on ML applications to tackle various issues ranging from chronic medical challenges (e.g., cardiological diseases) to patient data security. The findings of this research may be useful to policy makers and practitioners in the medical and healthcare sectors and to global research endeavours in the field. Future studies could include addressing issues such as growing ethical considerations, integration, and practical applications in wearable technology, IoT, and smart healthcare systems.

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