AI Innovations in Dentistry Programme 2026

Abstract

Modern pediatric dentistry increasingly demands an interdisciplinary approach to efficiently manage complex orofacial anomalies, developmental disorders and traumatic dental injuries, always considering the special demands of the pediatric patient. The combination of innovative technologies such as Erbium- and Diode lasers, cone-beam computed tomography (CBCT), microsurgical techniques, early orthodontic intervention, and AI-assisted diagnostics opens new possibilities for diagnosis and treatment planning in pediatric patients. The aim of this work is to illustrate the potential benefits of an advanced multidisciplinary treatment concept in pediatric dentistry through selected clinical cases and to highlight the synergies between the individual modalities. A case series of 12 pediatric cases with different indications was retrospectively analysed, involving CBCT-guided three-dimensional diagnosis of impacted, dislocated teeth or supernumerary teeth followed by laser-assisted surgical exposure with Er,Cr:YSGG (2780nm) and Diode Laser (940nm), orthodontic alignment with AI-based automated growth prediction from digital radiographs, and interdisciplinary management of trauma patients involving combined surgical-orthodontic therapy supported by laser-assisted wound and pulp tissue treatment, with follow-up ranging from 4 months to 3 years. In all cases, the combination of technologies resulted in more precise diagnostics, reduced treatment times and improved patient compliance. CBCT enabled exact three-dimensional localization of anatomical structures, while AI algorithms significantly supported diagnostic accuracy and the predictability of orthodontic growth patterns. Laser therapy markedly reduced intraoperative and postoperative discomfort. The multidisciplinary approach incorporating modern technologies represents a promising paradigm shift in pediatric dentistry. The integration of AI, imaging, surgery, and orthodontics fosters personalized, efficient, and child-friendly treatment. Further prospective studies with larger case numbers are necessary to confirm the long-term clinical relevance and cost-effectiveness of this integrative concept.

Learning Objectives

• Discuss the advantage of CBCT for the approach and procedure of minimally invasive laser-assisted surgery.
• Name three different therapy options of an impacted tooth.
• Discuss the treatment options available in cases of trauma with a root fracture.
• Explain the advantages of a multidisciplinary approach for complex cases in pediatric dentistry.
• Identify which laser wavelengths can be used for surgical procedures and the advantages of Erbium wavelengths.

Abstract

Modern clinics started years ago switching from conventional to intra oral impressions. Traditional workflows shifted over the years now towards digital and AI driven designs and guided surgery is now a norm. 

A modern dental laboratory should have transitioned by now and adapted to full digital workflow to keep up.

In my lecture and round table sessions I will show my participants how to design screw retained crowns, bridges, abutments and all on x cases from intra oral scans using exocad and the excellent MPS-System (Multi-Platform Systems).

Learning Objectives

This lecture addresses key design features in exocad.

• Enhance knowledge of digital design principles 
• Refine skills using simple and efficient workflows in exocad 
• Optimize implant prosthetic workflows utilizing the MPS system 
• Develop the ability to troubleshoot and resolve issues with intraoral scan data

Abstract

Artificial intelligence (AI) is transforming how clinicians perceive, interpret, and act upon diagnostic information. Yet the step from algorithmic output to informed clinical decision remains complex, requiring not only technical understanding but also critical reflection. This workshop explores how AI can augment human judgment in diagnostic and therapeutic processes, with a particular focus on multimodal data integration—from imaging and sensor signals to clinical and behavioral information.

Participants will gain hands-on experience with an AI-supported diagnostic tool, experimenting with real-world datasets and multimodal sensor inputs. Through guided exercises and group discussions, we will examine how AI-driven insights can inform, challenge, or refine clinical decisions. Beyond demonstrating current capabilities, the workshop will address limitations, transparency, and ethical dimensions of AI-assisted decision-making, as well as the need for what is now called „data literacy“.

By the end of the session, attendees will have developed a practical and conceptual understanding of how AI systems process complex diagnostic information and how clinicians can critically engage with these outputs to improve care quality and patient outcomes.

Abstract

This seminar equips clinicians with practical tools to integrate artificial intelligence into daily diagnostics without losing clinical authority.Participants begin with a clear, structured understanding of how AI works, what it can do reliably, and where its limitations lie. From there, the demo moves into real clinical application: intraoral scans as diagnostic sensor platforms, radiographic AI for bone and caries quantification, and cross-modal case interpretation combining scan, X-ray, and AI outputs. A dedicated strategic block addresses competitive positioning in high-performance markets, showing how diagnostic intelligence strengthens patient trust, supports premium positioning, and differentiates practices beyond simple technology ownership.

The demo concludes with integrated cases where participants align radiographic AI, scanner data, risk modeling, and orthodontic planning into structured decision architecture.It is a clinical seminar on using AI as a decision-support tool — not a decision-maker — to elevate diagnostic precision, professional confidence, and competitive positioning. The future of dentistry will not belong to those who work faster. It will belong to those who think better. 

Learning Objectives

• Interpret AI-supported radiographs with clinical accuracy and responsibility.
• Utilize 3D scan data for longitudinal monitoring of wear and risk progression.
• Translate risk probabilities into clear, actionable treatment thresholds.
• Recognize and mitigate automation bias in AI-assisted decision-making.
• Document AI-supported clinical decisions in a safe and compliant manner.
• Communicate quantified risk effectively to high-expectation patients.

Agenda

Supported by

Abstract

Contemporary dental treatment planning increasingly emphasizes a “begin with face” approach, recognizing the face as the primary reference for achieving optimal esthetic and functional outcomes. Traditional workflows often rely solely on intraoral landmarks, which may limit the predictability and harmony of the final result. In contrast, integrating facial analysis into treatment planning enables clinicians to align dental structures with overall facial proportions, enhancing both precision and patient-specific customization.

With the rapid advancement of digital dentistry, a wide range of technologies now support this face-driven workflow. Tools such as facial scanners, intraoral scanners, and cone beam computed tomography (CBCT) facilitate comprehensive data acquisition, while advanced software platforms enable integration, simulation, and design across multiple domains. These technologies improve diagnostic accuracy, streamline communication, and enhance treatment predictability.

This lecture provides a systematic overview of various digital dental tools and compares their capabilities in capturing measurements, analyzing anatomical relationships, and supporting treatment design. Emphasis is placed on how each modality contributes uniquely to the workflow, from facial reference acquisition to prosthetic planning, implant positioning, and orthodontic simulation.

By demonstrating practical applications across restorative dentistry, implantology, and clear aligner therapy, this session highlights how a face-driven digital workflow can be effectively implemented in daily clinical practice. The objective is to provide clinicians with a clear understanding of which tools to use, what parameters they can measure, and how they can be integrated to achieve precise, predictable, and esthetically driven outcomes.

Learning Objectives

1. Discuss the concept of “begin with face” and its importance in modern dental treatment planning.
2. Explain how facial reference integration improves predictability and precision in dental outcomes.
3. Describe the roles of key digital dental technologies, including facial scanners, intraoral scanners, and CBCT.
4. Compare the capabilities of different digital tools in measurement, analysis, and treatment design.
5. Demonstrate how to integrate multiple digital datasets into a cohesive, face-driven workflow.
6. Apply face-driven digital planning principles to restorative, implant, and orthodontic cases.

Abstract

Conventional implant surgery typically involves flap elevation to allow direct visualization of the underlying bone. Although effective, this approach may lead to increased postoperative pain, swelling, and delayed healing. With the advancement of digital dentistry, minimally invasive techniques—particularly flapless implant surgery—have become increasingly achievable through the use of digital surgical guides.

Modern workflows integrate intraoral scanning and cone beam computed tomography (CBCT) to capture both soft tissue and bone anatomy. These datasets are merged within planning software to create patient-specific surgical guides, enabling precise three-dimensional implant placement while minimizing surgical trauma. As a result, patients benefit from reduced discomfort and accelerated recovery.

In clinical practice, implant placement has traditionally relied on the experience and judgment of highly skilled clinicians—referred to here as “GuruGuide.” While clinical expertise remains invaluable, it does not always ensure consistency or precision, particularly in complex or high-volume cases where variability and human error may occur. In contrast, digital surgical guides provide a standardized and reproducible approach that enhances accuracy and reduces operator-dependent variability.

This lecture will compare the concepts of “GuruGuide” and Digital Surgical Guide, highlighting differences in predictability, workflow, and clinical outcomes. Various clinical scenarios, ranging from single-tooth implants to full-arch rehabilitation, will be presented to illustrate appropriate applications and limitations. The session aims to provide clinicians with a clear understanding of how digital guidance can complement clinical expertise to achieve more precise, minimally invasive, and predictable implant treatment outcomes.

Learning Objectives

1. Discuss the principles of minimally invasive implant surgery and the role of flapless techniques.
2. Explain the integration of intraoral scanning and CBCT in digital implant planning.
3. Compare Digital Guide and GuruGuide systems in terms of design, workflow, and clinical application.
4. Demonstrate the step-by-step workflow for creating and utilizing surgical guides in implant surgery.
5. Describe the indications and limitations of guided implant surgery in single-tooth and full-arch cases.
6. Apply digital guided surgery concepts to improve precision, reduce patient morbidity, and enhance clinical outcomes.

Abstract

Implant dentistry has traditionally focused on bone availability, implant positioning, and osseointegration. However, achieving optimal outcomes requires a broader perspective that extends beyond the bone to include overall facial profile and esthetic harmony. A facial driven design approach emphasizes planning implant treatment based on facial references, ensuring that functional rehabilitation also contributes to improved facial proportions and appearance.

Rather than considering implant placement in isolation, clinicians must adopt a multidisciplinary strategy that integrates implantology, restorative dentistry, and orthodontics. This approach enables coordinated modification of tooth position, occlusion, and jaw relationships to achieve a more balanced and esthetically pleasing facial outcome. Implant placement may also serve as anchorage for clear aligner therapy, further enhancing treatment efficiency and predictability.

Advances in digital dentistry have made this integrated workflow more accessible and precise. Technologies such as intraoral scanners, facial scanners, and cone beam computed tomography (CBCT), combined with advanced planning software, enable clinicians to visualize and simulate treatment outcomes prior to surgery. These tools facilitate comprehensive treatment planning that aligns dental structures with facial esthetics.

This lecture presents the concept of facial driven design in digital implant dentistry, with a focus on pre-surgical planning to optimize facial outcomes. By combining clear aligner therapy, restorative procedures, and implant placement within a unified digital workflow, clinicians can achieve highly predictable, efficient, and esthetically driven results. The session highlights how digital tools support this multidisciplinary approach, ultimately improving both functional and facial outcomes for patients.

Learning Objectives

1. Discuss the concept of facial driven design in implant dentistry and its clinical significance.
2. Explain the importance of integrating facial analysis into implant treatment planning.
3. Describe the multidisciplinary approach combining orthodontics, restorative dentistry, and implantology.
4. Demonstrate how digital tools, including intraoral scanners, facial scanners, and CBCT, support facial driven workflows.
5. Compare conventional implant planning with facial driven digital planning approaches.
6. Apply facial driven design principles to enhance esthetic and functional outcomes in implant cases.

Abstract

Artificial intelligence is expanding the clinical value of intraoral scanning beyond documentation and treatment planning. By analyzing scan data and highlighting potential oral health findings, AI-assisted tools can support clinicians in identifying surface and proximal caries, plaque, gingival recession, and tooth wear.

This lecture explores how visual overlays, structured assessments, and longitudinal scan comparisons can contribute to more consistent clinical evaluations and data-informed treatment decisions. Sequential scans may help clinicians monitor changes over time, assess disease progression, evaluate treatment outcomes, and communicate the importance of preventive care.

The presentation will also demonstrate how complex clinical information can be transformed into clear and accessible visual explanations for patients. Patient-facing digital tools can reinforce conversations in the dental practice, improve understanding, and encourage greater participation in treatment and prevention.

Through clinical examples and practical workflows, participants will gain insight into how AI-assisted intraoral analysis can enhance diagnostic confidence, strengthen patient communication, and support a more preventive, personalized, and patient-centered approach to oral healthcare. The lecture will also address the importance of responsible implementation, data quality, and continued clinical oversight when incorporating AI into everyday practice.

Learning Objectives

• Understand the foundations of clinical AI: Explain how artificial intelligence can be trained on large datasets of intraoral scans to support consistent identification of oral health findings.
• Recognize AI-assisted clinical findings: Explore the identification and visualization of surface caries, proximal caries, plaque, gingival recession, and tooth wear.
• Apply longitudinal monitoring: Understand how sequential intraoral scans can be compared to monitor changes, disease progression, and treatment outcomes over time.
• Strengthen patient communication: Use intuitive visual overlays and patient-facing digital tools to explain clinical findings and encourage greater patient understanding, engagement, and participation.
• Integrate AI responsibly into clinical practice: Recognize AI as a decision-support tool that complements, rather than replaces, professional clinical judgment.

Supported by

Abstract

Advances in digital technology are transforming the way clinicians diagnose, plan, and manage complex endodontic cases. From intricate root canal anatomy to compromised retreatment cases with procedural errors, technology-enhanced workflows have significantly improved precision, efficiency, and treatment predictability.

This lecture will present a practical, evidence-based approach to managing complex primary treatments and retreatments through the integration of advanced technologies, including CBCT & Artificial Intelligence aided imaging, dental operating microscopy, ultrasonic instrumentation, laser irrigation solution activation and guided endodontic techniques. Emphasis will be placed on the diagnosis and management of challenging anatomical variations, as well as the correction of common procedural complications such as missed canals, separated instruments, perforations and inadequate previous treatments.

Through a series of clinically documented cases, participants will gain practical insights into treatment planning, decision-making and minimally invasive strategies that preserve tooth structure while maximizing long-term outcomes. The lecture aims to demonstrate how technology-enhanced endodontics enables clinicians to transform complex clinical challenges into predictable, functional and biologically sound treatment outcomes.

Learning Objectives

• Develop a structured approach to treatment planning and clinical decision-making for complex primary endodontic treatments and retreatment cases.
• Recognize and manage complex root canal anatomies using contemporary digital technologies and advanced clinical techniques.
• Identify and correct common procedural errors, including missed canals, separated instruments and perforations.
• Integrate CBCT, dental operating microscopy, ultrasonic instrumentation, and guided endodontic techniques into daily clinical practice.

Supported by:

Abstract

Current intraoral scanning technologies primarily capture static anatomical data, while functional occlusion remains largely inferred. Emerging innovations aim to bridge this gap through real-time functional scanning, combining motion tracking, AI, and advanced sensors. This lecture explores the evolution of scanning technologies toward dynamic occlusion capture, discussing their potential to redefine diagnosis, prosthetic design, and implant success prediction.

Learning Objectives

• Explain the Evolution of intraoral scanning
• Understand the limitations of static intraoral scanning
• Explore emerging technologies in real-time functional scanning
• Evaluate clinical applications of dynamic occlusion capture
• Anticipate future developments in digital dentistry