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- Artificial Intelligence applied to implant surgical planning: a systematic reviewPublication . Molcard, Arthur Charles Félix; Durão, Ana Paula ReisPrior assessment of bone quality is a critical factor in implant dentistry, influencing implant stability, osseointegration, and long-term success. Conventional approaches, including conebeam computed tomography (CBCT) imaging and tactile perception, are often qualitative and variable. Artificial intelligence (AI) can help with the development of automated, objective, and reproducible analyses. The present systematic review aims to present the current evidence on AI-driven bone quality assessment (BQA) and its impact on implant planning and clinical outcomes. The search strategy followed the PRISMA guidelines and included several electronic databases: PubMed, IEEE Xplore, Web of Science, and ScienceDirect. Included studies were peer reviewed and published between 2019 and February 2025. The primary objectives were to determine the efficacy and consistency of AI in the assessment of bone quality, compare it with traditional methods, and analyse its influence on clinical decision-making and implant success. The Joanna Briggs Institute (JBI) Critical Appraisal Checklists were used to evaluate the risk of bias (RoB). As a result, thirteen studies met the inclusion criteria. AI models, particularly deep learning (DL) approaches, showed strong performance in estimating bone quality parameters, such as bone mineral density (BMD), cortical thickness and trabecular pattern. AI-based approaches led to more accurate and less biased bone assessments than conventional methods. Some studies indicated that using AI tools may contribute in better implant site identification (based on quality) and potentially improve clinical results. The application of AI techniques for BQA is a promising approach for enhancing the diagnostic accuracy and clinical decision-making in dental implantology. AI can increase the standardisation of bone assessments and thus improve the predictability of implant treatments outcomes. More research is required to confirm these findings in broader clinical settings and to investigate the implementation of such AI technologies.
- The use of simulation tools in oral and maxillofacial surgery: a systematic reviewPublication . Pastore, Ludovico; Venda Nova, Carolina; Correia, Patrícia NunesBackground: Oral and maxillofacial surgery is a challenging specialty with challenging technical demands and fine anatomical requirements. Traditional teaching methods are nearly entirely reliant on direct observation, cadaveric dissection, and clinical supervision. Limitations such as restricted access to cases, ethical considerations, and lack of consistency in learning opportunities are also present in these methods. Simulation technologies such as immersive virtual reality (VR), 3D printed anatomical models, dynamic navigation systems, and haptic feedback devices offer innovative solutions with the potential to enhance surgical training through interactive, reproducible, and risk-free learning environments. The purpose of this systematic review is to evaluate how these simulation devices compare to conventional teaching methods in oral and maxillofacial surgical training. Methodology: The literature search was carried out in three main databases: PubMed, Web of Science and Google Schoolar. The PRISMA guidelines were followed and inclusion and exclusion criteria were applied. Results: Of the 440 studies identified, 11 studies from 2014 to 2024 were selected and reviewed. Various simulation tools utilized were immersive virtual reality, 3D printed models, dynamic navigation systems, and haptic devices. Simulation technology has shown utility in preclinical education, particularly in augmenting student confidence, satisfaction, and anatomical knowledge. Variability of findings, paucity of longitudinal data, and scarcity of randomized controlled trials restrict the potential for generalized conclusions. The majority of studies addressed subjective outcomes, with few assessing objectively technical accuracy or translation to clinical practice. Conclusions: Simulation devices have great educational promise, but they are not yet available for substitution of conventional teaching. Additional well-conducted investigations involving larger numbers of subjects and longer follow-up are required to authenticate these technologies and establish their ultimate position in oral and maxillofacial surgery training curricula.