## 4 Discussion  
This systematic review and meta-analysis was conducted to compare the effectiveness of artificial intelligence and manual methods in identifying anatomical landmarks. The goal of this study was to examine the discrepancies in the identification of anatomical landmarks between artificial intelligence and manual approaches. The results of the study showed that artificial intelligence was just as effective as manual methods in detecting anatomical landmarks in both cephalometric radiographs and CBCT. As manual landmarking is a labor-intensive task, automated detection of landmarks could be greatly beneficial, as it expedites access to cephalometric analysis [22]. Clinicians, particularly those who are not experts in the field, may significantly benefit from using AI for evaluating disorders [5]. Automated landmark identification can expedite the manual analysis process for dental professionals, thereby enabling more prompt diagnosis and treatment planning [14]. Leveraging AI technology in the diagnostic procedure, dental practitioners can optimize their workflow, resulting in a more efficient journey from diagnosis to treatment [23]. The performance of AI models is not solely determined by their ability to provide accurate or complete information, but also by how well they engage, assist, and satisfy users, too [24]. AI may identify anatomical points more consistently, which minimizes variability that can arise from manual identification by practitioners with varying levels of expertise [25]. Presently, AI-based automatic diagnosis techniques primarily function as assistant tools, and the need to make judgments or adjustments to AI results from this [26]. Cephalometric analysis is a quantitative diagnostic tool that is frequently utilized by orthodontists to assess skeletal and dentoalveolar relationships, morphometric characteristics, and growth patterns of their patients [22]. To address the limitations of two-dimensional radiographs, such as the superimposition of left and right cranial structures, unequal magnification of bilateral structures, and the potential for distortion of mid-facial structures, three-dimensional CBCT has been implemented to assess craniofacial structures with reduced distortion compared to conventional radiographic images [12]. CBCT is an advanced radiological method that generates detailed three-dimensional reconstructions and slice images [20]. As familiarity with CBCT images grows within the field, diagnostic techniques that optimize their potential are being developed, and over time, through a process of trial and error, the full capabilities of CBCT will be more fully understood and realized [27]. The customary method of evaluating the performance of an automated identification system has been to assess its ability to successfully detect skeletal landmarks with a variance of  
Figure 7: Funnel plot without added studies illustrating the publication bias analysis. 2 mm, which has historically been considered a clinically acceptable range of error at AI performance contests [14]. According to another report, the average automated detection error was 1.36 \(\pm\) 0.98 mm [16]. This study showed that the use of artificial intelligence was as accurate as the manual approach when it comes to detecting anatomical landmarks. Establishing the gold standard of the anatomical landmarks in cephalomaetic radiographs or CBCT differed between the studies. In one study, the gold standard was defined as the mean value of the positions of the landmarks identified two orthodontists with clinical experience [16]. In another study, the initial manual identification of cephalometric landmarks on CBCT-synthesized PA images was established as the standard of truth [17]. The initial annotation of the points was performed by students, and subsequently, an orthodontist and maxillofacial surgeon collaborated to review and rectify the positions of the points [15]. The mean inter-examiner difference of the landmarks identified by the orthodontists was 1.31 \(\pm\) 1.13 mm [16].