Automating surgical guide design for single anterior implants powered by artificial intelligence

Implant dentistry has undergone a significant transformation with the integration of artificial intelligence (AI) into digital workflows. AI, a branch of computer science that enables machines to simulate human cognitive processes, has demonstrated the potential to improve the accuracy, efficiency, and consistency of dental implant procedures. In guided implant surgery, the design of surgical guides traditionally requires manual segmentation of anatomical structures and alignment of cone-beam computed tomography and surface scan data – a process that is time-consuming and operator-dependent.

This clinical report presents an AI-powered static computer-assisted implant surgery workflow for a 25-year-old man requiring a single anterior implant. By using deep learning architectures for automated data processing, the digital guide design was completed within 20 minutes. This case demonstrates the application of AI beyond diagnostic support, extending to automated surgical guide design for accurate implant placement and immediate previsualisation in the high-stakes aesthetic zone. While AI-enhanced workflows demonstrate a promising approach to improving predictability, clinicians must maintain rigorous manual verification to mitigate risks associated with data artefacts and algorithmic limitations.

By automating the traditionally labour-intensive segmentation and alignment of cone-beam computed tomography and standard tessellation language data, artificial intelligence (AI)-driven workflows can condense the pre-surgical planning phase to under twenty minutes without compromising accuracy.

This report highlights how AI serves as a primary design tool for anterior implants, providing the precision required to achieve sufficient primary stability, allowing for immediate, screw-retained previsualisation in the aesthetic zone.

Despite the high reproducibility of deep learning models, practitioners must remain the final ‘checkpoint' to manually verify automated datasets, ensuring that ‘black box' algorithmic errors or imaging artefacts do not translate into clinical placement errors.

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The data that support the findings of this clinical report are available from the corresponding author upon reasonable request. The data are not publicly available due to ethical restrictions and the need to protect patient confidentiality.

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Boston University, Henry M. Goldman School of Dental Medicine, 635 Albany St, Boston, USA

Virat Hansrani, Elias Exarchos, Jae Choi & Peixi Liao

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VH drafted the initial manuscript, while EE, JC and PL revised it critically for important intellectual content. All authors have given final approval of the version to be published and agree to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

Correspondence to Virat Hansrani.

No competing interest to declare.

Written informed consent was obtained from the patient for treatment and for the publication of this case, including clinical photographs and radiographs.

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Hansrani, V., Exarchos, E., Choi, J. et al. Automating surgical guide design for single anterior implants powered by artificial intelligence – a clinical report. Br Dent J 241, 169–173 (2026). https://doi.org/10.1038/s41415-026-9889-y

Version of record: 14 August 2026

DOI: https://doi.org/10.1038/s41415-026-9889-y