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Digital Technologies in Prosthetic Dentistry: Current Trends, Clinical Applications and Future Directions - A Narrative Review


Authors : Onwudi, Henry I.; Ejeta K. O.; Eyarefe S. O.

Volume/Issue : Volume 11 - 2026, Issue 9 - September


DOI : https://doi.org/10.38124/ijisrt/26sep1462

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.


Abstract : Background: The integration of digital technologies in prosthetic dentistry has precipitated a transformative shift from empirical craftsmanship to precision-driven, data-informed clinical practice. Despite rapid technological proliferation, a consolidated, clinically oriented synthesis of these advances remains needed.  Objective: This narrative review critically evaluates the current landscape of digital prosthetic dentistry, encompassing intraoral scanning (IOS), Computer-Aided Design/Computer-Aided Manufacturing (CAD/CAM), subtractive and additive manufacturing, artificial intelligence (AI), and emerging augmented reality (AR) platforms.  Methods: This narrative review discusses peer-reviewed research on intraoral scanning, CAD/CAM, additive manufacturing, artificial intelligence, and guided implant surgery. The cited literature was checked against publisher or PubMed records; findings are described according to the populations and methods studied.  Results: Intraoral scanning can improve patient comfort and support efficient fixed prosthodontic workflows, although accuracy depends on indication and scan extent. Digital complete dentures show clinically acceptable adaptation in many laboratory studies, while guided implant surgery has measurable deviations between planned and placed implants. Evidence for AI and newer printable materials remains heterogeneous (Ahlholm et al., 2018; Mangano et al., 2017; Wang et al., 2021; Tahmaseb et al., 2018; Schwendicke et al., 2020).  Conclusion: The convergence of AI, additive manufacturing, and augmented reality represents the proximate frontier in personalised dental rehabilitation. Strategic investment in hybrid fabrication infrastructure, standardised curricula reform, and robust longitudinal outcome data is urgently required to guide evidence-based adoption.

Keywords : Digital Dentistry; CAD/CAM; Intraoral Scanning; Additive Manufacturing; Artificial Intelligence; Prosthetic Dentistry; Dentistry 4.0; Zirconia; 3D Printing; Virtual Patient.

References :

  1. Ahlholm, P., Sipilä, K., Vallittu, P., Jakonen, M., & Kotiranta, U. (2018). Digital versus conventional impressions in fixed prosthodontics: A review. Journal of Prosthodontics, 27(1), 35–41. https://doi.org/10.1111/jopr.12527
  2. Alghazzawi, T. F. (2016). Advancements in CAD/CAM technology: Options for practical implementation. Journal of Prosthodontic Research, 60(2), 72–84. https://doi.org/10.1016/j.jpor.2016.01.003
  3. Dawood, A., Marti Marti, B., Sauret-Jackson, V., & Darwood, A. (2015). 3D printing in dentistry. British Dental Journal, 219(11), 521–529. https://doi.org/10.1038/sj.bdj.2015.914
  4. The glossary of prosthodontic terms: Ninth edition. (2017). Journal of Prosthetic Dentistry, 117(5S), e1–e105. https://doi.org/10.1016/j.prosdent.2016.12.001
  5. Mangano, F., Gandolfi, A., Luongo, G., & Logozzo, S. (2017). Intraoral scanners in dentistry: A review of the current literature. BMC Oral Health, 17, 149. https://doi.org/10.1186/s12903-017-0442-x
  6. Schwendicke, F., Samek, W., & Krois, J. (2020). Artificial intelligence in dentistry: Chances and challenges. Journal of Dental Research, 99(7), 769–774. https://doi.org/10.1177/0022034520915714
  7. Tahmaseb, A., Wu, V., Wismeijer, D., Coucke, W., & Evans, C. (2018). The accuracy of static computer-aided implant surgery: A systematic review and meta-analysis. Clinical Oral Implants Research, 29(S16), 416–435. https://doi.org/10.1111/clr.13346
  8. Wang, C., Shi, Y.-F., Xie, P.-J., & Wu, J.-H. (2021). Accuracy of digital complete dentures: A systematic review of in vitro studies. Journal of Prosthetic Dentistry, 125(2), 249–256. https://doi.org/10.1016/j.prosdent.2020.01.004

Background: The integration of digital technologies in prosthetic dentistry has precipitated a transformative shift from empirical craftsmanship to precision-driven, data-informed clinical practice. Despite rapid technological proliferation, a consolidated, clinically oriented synthesis of these advances remains needed.  Objective: This narrative review critically evaluates the current landscape of digital prosthetic dentistry, encompassing intraoral scanning (IOS), Computer-Aided Design/Computer-Aided Manufacturing (CAD/CAM), subtractive and additive manufacturing, artificial intelligence (AI), and emerging augmented reality (AR) platforms.  Methods: This narrative review discusses peer-reviewed research on intraoral scanning, CAD/CAM, additive manufacturing, artificial intelligence, and guided implant surgery. The cited literature was checked against publisher or PubMed records; findings are described according to the populations and methods studied.  Results: Intraoral scanning can improve patient comfort and support efficient fixed prosthodontic workflows, although accuracy depends on indication and scan extent. Digital complete dentures show clinically acceptable adaptation in many laboratory studies, while guided implant surgery has measurable deviations between planned and placed implants. Evidence for AI and newer printable materials remains heterogeneous (Ahlholm et al., 2018; Mangano et al., 2017; Wang et al., 2021; Tahmaseb et al., 2018; Schwendicke et al., 2020).  Conclusion: The convergence of AI, additive manufacturing, and augmented reality represents the proximate frontier in personalised dental rehabilitation. Strategic investment in hybrid fabrication infrastructure, standardised curricula reform, and robust longitudinal outcome data is urgently required to guide evidence-based adoption.

Keywords : Digital Dentistry; CAD/CAM; Intraoral Scanning; Additive Manufacturing; Artificial Intelligence; Prosthetic Dentistry; Dentistry 4.0; Zirconia; 3D Printing; Virtual Patient.

Paper Submission Last Date
31 - October - 2026

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