## 1 Introduction  
Artificial intelligence (AI) has recently undergone a remarkable evolution, transitioning from speculative theory to a central component of modern technology. In the medical field, and specifically in orthodonics, AI has gained attention, being used in combination with teledentistry to accelerate diagnostics, optimize therapeutic decision making, and manage large-scale health data with improved accuracy and efficiency [1, 2, 3, 4, 5]. In addition to these primary functions, AI has also been applied to other areas such as skeletal maturity assessment, temporomandibular joint (TMJ) evaluation, and orthognathic treatment planning. Among AI applications, remote monitoring has emerged as particularly promising, as it supports the ongoing evaluation of patient progress without the need for frequent in-office appointments [6, 7, 8, 9, 10, 11, 12, 13, 14]. Remote monitoring software could allow the use of smartphone-generated photographs and videos, and contact with patients, without the necessity of an immediate in-office visit. In this way, clinicians could monitor rapid maxillary expansion, inter-maximillary elastics compliance, and oral hygiene conditions, as well as manage orthodontic emergencies such as debonded brackets or tubes and problems with arch-wire ligatures. Remote monitoring could also be a helpful tool in the retention phase [7; 15; 16; 17; 18]. Within this framework, a software application that uses AI to help the clinician to remotely monitor patients during their orthodontic treatment has been developed in France; Dental Monitoring(r) (DM, Dental Monitoring, Paris, France) is a remote monitoring software which uses innovative AI-driven remote monitoring (AIDRM) technology to assess tooth movements during orthodontic treatment [19]. Conceptually, remote monitoring platforms such as DM can be framed within the theory of multi-agent systems (MAS), in which patients, clinicians, and software continuously interact through data exchange and feedback loops. This dynamic structure should enable adaptive treatment adjustments and promote patient-specific clinical pathways, aligning with cybernetic models of digital health systems--that is, intelligent systems based on continuous feedback loops, through which data is constantly collected, analyzed, and used to regulate and optimize clinical processes in real time [1]. At the beginning of their remotely monitored orthodontic treatment, patients are instructed to download the Dental Monitoring application on their smartphone, and to use \"Scanbox\" DM cheek-retractors (Dental Monitoring, Paris, France) to help them take good-quality intra-oral pictures using their smartphone. During their orthodontic treatment, patients receive notifications from the DM smartphone application prompting them to provide a DM scan; the scanning frequency depends on the clinician, who determines an initial customized protocol of remote monitoring. The DM's algorithm extracts information from the pictures taken by the patient and compares it to the initial impression [19]. Dental Monitoring was first introduced to assess orthodontic treatment with clear aligners (CA). Once the patient has taken intra-oral pictures with and without CAs, the software analyzes CA fit and informs the patient if it is time to switch to the following aligner in the series, or whether it is advisable to continue wearing the same aligner pending reassessment of repeat DM photos after few days [20; 21]. Thus far, very few studies have evaluated the use of DM in orthodontic treatment. [PERSON] and colleagues have compared two groups of patients with similar baseline malocclusions treated via CAs, one using DM and the other not (controls). Treatment duration, number of refinements, number of refinement aligners, time to first refinement, and number of appointments were analyzed and compared between groups. The only variable found to be statistically significant was the number of appointments, which was lower in the DM group with respect to controls [20].