###### Abstract  
Background: Researchers have noted that the advent of artificial intelligence (AI) heralds a promising era, with potential to significantly enhance diagnostic and predictive abilities in clinical settings. The aim of this meta-analysis is to evaluate the discrepancies in identifying anatomical landmarks between AI and manual approaches. Methods: A comprehensive search strategy was employed, incorporating controlled vocabulary (MeSH) and free-text terms. This search was conducted by two reviewers to identify published systematic reviews. Three major electronic databases, namely, Medline via PubMed, the Cochrane database, and Embase, were searched up to May 2024. Results: Initially, 369 articles were identified. After conducting a comprehensive search and applying strict inclusion criteria, a total of ten studies were deemed eligible for inclusion in the meta-analysis. The results showed that the average difference in detecting anatomical landmarks between artificial intelligence and manual approaches was 0.35, with a 95% confidence interval (CI) ranging from \(-\)0.09 to 0.78. Additionally, the overall effect between the two groups was found to be insignificant. Upon further analysis of the subgroup of cephalometric radiographs, it was determined that there were no significant differences between the two groups in terms of detecting anatomical landmarks. Similarly, the subgroup of cone-beam computed tomography (CBCT) revealed no significant differences between the groups. Conclusions: In summary, the study concluded that the use of artificial intelligence is just as effective as the manual approach when it comes to detecting anatomical landmarks, both in general and in specific contexts such as cephalometric radiographs and CBCT evaluations.  
Keywords:anatomic landmarks; artificial intelligence; diagnosis; orthodontics +
Footnote †: journal: Medicalthe potential to greatly enhance diagnostic and predictive capabilities in clinical settings [2]. AI has made considerable progress in various aspects of dentistry, presenting promising developments for both dental professionals and patients alike [3]. In dentistry, AI has been utilized to aid in the acquisition of digital data, including the tasks of scan cleaning, scan assistance, and the automation of the alignment process between the scanned body of an implant and the planning procedures for implants [4]. Deep learning models have proven to be highly effective in detecting temporomandibular joint arthopathies with a high degree of sensitivity and specificity [5].  
Similarly, AI has made considerable advancements in orthodontics, and this area has generated significant interest among orthodontists [6]. AI can aid in diagnosing orthodontic issues by analyzing clinical photographs, radiographs, and three-dimensional scans [7]. AI can identify various irregularities, including those related to tooth alignment, jaw relation, and tooth structure [8]. The use of AI in this context expedites the diagnostic process and enhances the accuracy of the results [9]. This study aimed to carry out a meta-analysis to evaluate the disparities in the identification of anatomical landmarks between artificial intelligence and manual approaches.