Bibliometric Analysis of the Role of Artificial Intelligence in Detecting Maxillofacial Fractures

Abstract

Facial bone fractures are a common occurrence in trauma cases, particularly in India where road traffic accidents contribute significantly. Over the past few years, artificial intelligence (AI) has become a potent instrument to help medical professionals diagnose and treat facial fractures. This study aims to perform a bibliometric analysis, that is, a quantitative and qualitative analysis, of publications focusing on the role of AI in detecting facial bone fractures. Bibliometric analysis can be a very strong measure of research productivity and analysis of trends within a given area of research. Data were drawn from the Dimensions AI database; 58 relevant scientific articles were analyzed in this study. This bibliometric analysis aims to assess the volume of research in this area, identifying key trends, authors, institutions, and countries contributing to the literature. The Dimensions AI database was used to gather and analyze relevant data, shedding light on the research impact through indicators such as the h-index, citation counts, and publication trends. This review will depict the landscape of research work, highlighting the rising influence of the use of AI for accurate diagnostics in facial bone fractures and further detailing gaps and potential avenues of future research directed toward solutions such as standardizing datasets and clinical integration.

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