The use of AI in the diagnosis of oral diseases: a bibliometric analysis

This study presents a comprehensive bibliometric analysis of artificial intelligence (AI) applications in the diagnosis of oral diseases, aiming to explore publication trends, identify key contributors, and highlight emerging research directions. Utilizing data from the Web of Science and Scopus databases, we analyzed 90 relevant publications after refining an initial pool of 179 articles. The results reveal a significant increase in research output since 2016, with a peak in 2023, followed by a decline in 2024. Early research, dating back to 1996, focused on decision-support systems, while recent advancements emphasize deep learning, machine learning, and radiographic imaging for improved diagnostic accuracy. Keyword analysis identified "artificial intelligence," "deep learning," and "diagnosis" as central themes, reflecting the field's focus on technological innovation in oral healthcare. Geographically, China, the United States, and India emerged as leading contributors, with strong international collaborations observed between these countries and regions such as Europe and the Middle East. Despite the growing interest, challenges such as funding constraints and regulatory hurdles may have contributed to the recent decline in publications. This study underscores the transformative potential of AI in oral disease diagnosis and provides a roadmap for future research, emphasizing the need for global collaboration, inclusivity, and continuous monitoring of emerging trends to advance precision dentistry.