Artificial intelligence’s impact on oral healthcare in terms of clinical outcomes: a bibliometric analysis

Abstract

Purpose

This study provides a comprehensive overview of the impact of artificial intelligence (AI) applications on oral healthcare, focusing on clinical outcomes.

Design/methodology/approach

A systematic approach was used to gather articles from databases such as Scopus, ScienceDirect, PubMed, Web of Science and Google Scholar from 2010 to 2024. The selection criteria included articles published in English, focusing solely on clinical applications of AI in dentistry. Articles such as conference proceedings, editorial material and personal opinions were excluded. The articles were analyzed and visualized using Rayyan software, Microsoft Excel and VOSviewer.

Findings

Results indicate that 120 publications were authored by 58 scholars from 92 institutions across 29 countries, with a notable surge since 2018. This analysis showed the significant emphasis on the use of deep learning, demonstrating its high accuracy and performance in oral healthcare, often exceeding that of dentists. It also proved that even though AI is sometimes seen as an auxiliary tool, many studies revealed that AI has a performance near dental professionals’ levels. Findings concluded that the majority of studies indicate that AI is generating better clinical outcomes in oral healthcare.

Practical implications

This study provides dental professionals with insights on integrating AI for better diagnosis and treatment. Policymakers and healthcare institutions can use these findings to inform AI adoption and training strategies.

Originality/value

It presents novel and valuable findings that can benefit various stakeholders by shedding light on the present scenario and potential future paths of AI integration in oral healthcare, contributing to its overall advancement.

Citation

AlQaifi, F., Tengilimoglu, D. and Arslan Aras, I. (2024), "Artificial intelligence’s impact on oral healthcare in terms of clinical outcomes: a bibliometric analysis", Journal of Health Organization and Management, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/JHOM-06-2024-0233