# Robots at the Chairside: A Narrative Review of Robotic-Assisted Surgery Revolutionizing Dental Implantology in the Digital Dentistry Era

## TL;DR

Robotic-assisted surgery is transforming dental implantology by improving precision, safety, and outcomes through advanced technologies like AI and 3D imaging.

## Contribution

This paper reviews the evolution and impact of robotic systems in dental implantology, highlighting their novel integration of haptic feedback and AI for enhanced surgical precision.

## Key findings

- Robotic systems like Yomi and Yakebot reduce human error and improve implant positioning accuracy.
- AI and machine learning enable adaptive responses to patient movement and bone density variations.
- Robotic platforms support minimally invasive techniques and complex procedures like full-arch rehabilitation.

## Abstract

The advent of robotic-assisted technologies has introduced a paradigm shift in dental implantology, aiming to enhance surgical precision, safety, and predictability. Robotic implant surgery integrates advanced imaging, computer-aided planning, and mechanized execution to overcome the inherent limitations of conventional freehand and static/dynamic navigation techniques. Traditional implant placement is heavily dependent on clinician experience, tactile feedback, and intraoperative judgment, which may result in deviations in implant positioning, prosthetic misalignment, or damage to vital structures. Robotic systems, such as Yomi (Neocis, Miami, FL, USA), Remebot (Beijing Baihui Weikang Technology Co., Ltd., Beijing, China), and Yakebot (Beijing Yakebot Technology Co., Ltd., Beijing, China), utilize haptic, visual, and combined feedback mechanisms to guide implant osteotomy and placement, thereby reducing human error, hand tremors, and operator fatigue.

Robotic platforms employ either semi-active or fully autonomous modalities, each offering distinct levels of surgical autonomy while maintaining surgeon oversight. Semi-active systems provide positional constraints and guidance, allowing surgeons to control drill motion within pre-planned trajectories, whereas active systems can execute osteotomies and implant insertion autonomously under real-time monitoring. These systems leverage three-dimensional imaging, cone-beam computed tomography (CBCT), optical markers, and force/torque sensors to achieve sub-millimetric accuracy in implant positioning. Furthermore, integration of artificial intelligence and machine learning algorithms enables adaptive responses to patient movement, variations in bone density, and thermal monitoring, simulating the tactile perception of experienced surgeons.

Beyond accuracy, robotic systems offer advantages in minimally invasive surgery, including reduced tissue trauma, improved flapless approaches, decreased surgical time in experienced hands, and enhanced ergonomics for clinicians. Moreover, robotic platforms hold potential for complex procedures such as full-arch rehabilitation and zygomatic implant placement, where conventional techniques pose greater risk. In conclusion, robotic-assisted dental implantology represents a transformative advancement, combining technological innovation with surgical precision to enhance clinical outcomes. Continued development and rigorous clinical evaluation are imperative to maximize its utility, broaden accessibility, and facilitate the transition from experimental to standard practice in contemporary implant dentistry.