Computer‐Assisted Implant Surgery: Patients' Experience and Perspectives - Xin Hui Yeo; Lin Jing Uei; Man Yi; Kajorn Kungsadalpipob; Keskanya Subbalehka; Bilal Al‐Nawas; Nikos Mattheos (Clinical and Experimental Dental Research, 2025)
ABSTRACT Objectives Although computer‐assisted implant surgery (CAIS) has increased significantly the precision of dental implant placement, documentation of the impact of such technologies in the patient‐reported experience and outcomes remains, however, limited. The aim of this white paper was to assess the impact of CAIS on key aspects of the patient experience, such as its potential benefits on (1) patients' understanding and engagement with implant surgery, (2) patient's confidence with treatment outcomes, (3) patients' preferences, (4) intra‐ and (5) Postoperative experience and (6) long‐term patient‐reported outcomes and oral health‐related quality of life. Material and Methods A review of the literature compiled existing evidence from clinical studies up to November 2024, which was later discussed and synthesized with expert opinions and the best currently documented experience and practice. Results No evidence was found that CAIS improves patient engagement or confidence with treatment outcomes, while comparative studies showed no difference in the intra‐ and postoperative experience when CAIS is used. Impact of the cost of CAIS procedures on patients perceptions and preferences has also not been explored, with the majority of studies significantly subsidizing patient costs, in particular randomized trials. At the same time, studies that compare interventions cannot assess the overall benefits of a complex workflow such as immediacy or minimally invasive approaches, to which CAIS is an essential part. Conclusions Research on patient outcomes with CAIS might not fully reflect the potential of these technologies when limited to the level of the surgical intervention. Major anticipated benefits of CAIS for the patient such as the potential to reduce complexity and facilitate faster, safer and more predictable execution of digitally designed treatments, could be better approached in the future by studies aimed at assessing patient‐reported outcomes from entire treatment workflows.