Geistlich webinar to explore AI and precision approaches in implant dentistry

Prof. Giannobile, what applications of AI currently provide the greatest practical benefit in routine implant dentistry?
The clearest benefits can be seen in radiographic image analysis, automated anatomical segmentation of teeth and alveolar ridges, implant positioning, prosthetic design and workflow efficiency. These applications reduce repetitive work and improve consistency—although the clinician must validate every output.

In your opinion, in which areas are current AI applications reliable, and what are their limitations in predicting implant success and long-term prognosis?
AI can perform well in narrowly defined diagnostic and implant planning tasks, but it is less reliable at predicting implant survival or long-term prognosis because outcomes depend on biological, behavioural, prosthetic and surgeon-related factors. Current models should therefore support—not replace—clinical judgement.

What are the main limitations or challenges of integrating AI into contemporary implant dentistry, and how should clinicians approach AI-supported decisions in terms of clinical autonomy and professional responsibility?
Major challenges include biased or unrepresentative datasets, limited external validation, poor explainability, interoperability issues and the possibility of automation bias. Clinical autonomy and responsibility remain with the treating professional, including the implant surgeon and the restorative dentist or prosthodontist. He or she must understand the system’s limitations and be able to override AI recommendations, especially when these contain glaring errors.

What safeguards and conditions must be in place for AI-powered systems to be integrated safely and reproducibly into routine dental practice and in compliance with patient data protection requirements?
Systems need rigorous multicentre validation through practice networks or university-based clinical research, as well as regulatory oversight, transparent performance boundaries, secure data governance, informed consent where appropriate and continuous monitoring after deployment. Practices also require staff training, interoperable record systems, audit trails and clear accountability when an AI-supported decision causes harm.

How could AI and patient-specific biological information influence regenerative decisions around implant therapy?
By combining radiographic imaging with systemic health, genetics, inflammatory biomarkers and healing history, AI could help estimate regenerative potential and tailor the extent of bone or soft-tissue augmentation, the selection of biomaterials or the use of biologic agents such as growth factors. This represents a promising approach to precision dental medicine, but prospective clinical trials must demonstrate that such personalisation improves outcomes before it becomes standard care. Our team has been involved in many such studies over the years.