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<p>This systematic review assesses the integration of artificial intelligence (AI) in the surface engineering of dental implants, with a focus on biomaterials and clinical translation. AI-driven methods, including machine learning and predictive analytics, enable the tailoring of implant surface properties and optimisation of host responses, surpassing traditional modification techniques. Advanced biomaterials such as hydroxyapatite coatings and ion-doped surfaces enhance osseointegration, antimicrobial resistance, and stability. AI-guided design accelerates the development of multi-functional, bioactive coatings and supports personalised surface chemistries for clinical needs. Translational challenges persist in validating AI frameworks, reproducing laboratory outcomes <i>in vivo</i>, and demonstrating long-term benefits. The review also explores AI-enabled adaptive coatings responsive to physiological cues and calls for standardised protocols and clinical trials to ensure reliable translation from bench to bedside. Ultimately, convergence between AI, biomaterials, and translational research will enable bio-responsive dental implants that ensure patient-specific clinical success.</p> Graphical abstract <p></p>

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A review on translational smart biomaterials for dental implants: AI-driven surface engineering and advanced coatings

  • 期刊论文

  • 发表日期：

    2026年4月24日

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  • Prachi Palta,
  • Aastha Palta,
  • Virinder Kumar

摘要

This systematic review assesses the integration of artificial intelligence (AI) in the surface engineering of dental implants, with a focus on biomaterials and clinical translation. AI-driven methods, including machine learning and predictive analytics, enable the tailoring of implant surface properties and optimisation of host responses, surpassing traditional modification techniques. Advanced biomaterials such as hydroxyapatite coatings and ion-doped surfaces enhance osseointegration, antimicrobial resistance, and stability. AI-guided design accelerates the development of multi-functional, bioactive coatings and supports personalised surface chemistries for clinical needs. Translational challenges persist in validating AI frameworks, reproducing laboratory outcomes in vivo, and demonstrating long-term benefits. The review also explores AI-enabled adaptive coatings responsive to physiological cues and calls for standardised protocols and clinical trials to ensure reliable translation from bench to bedside. Ultimately, convergence between AI, biomaterials, and translational research will enable bio-responsive dental implants that ensure patient-specific clinical success.

Graphical abstract