BACKGROUND: This study compared the accuracy (defined by trueness and precision) of implant placement between a semi-autonomous robotic system (SARS) and a dynamic navigation system (DNS) in completely edentulous models, evaluating the influence of a...
BACKGROUND: This study aims to evaluate and compare the performance of five publicly accessible large-language-model (LLM) based chatbots-ChatGPT-o1, Deepseek-R1, Google-Gemini-Advanced, Claude-3.5-Sonnet, and Perplexity-Pro-in providing responses to...
BACKGROUND: The integration of artificial intelligence (AI) in dental implant planning has emerged as a transformative approach to enhance diagnostic accuracy and efficiency. This study aimed to evaluate the performance of two object detection models...
BACKGROUND: Recent advancements in Artificial Intelligence (AI) have transformed the healthcare field, particularly through chatbots like ChatGPT, OpenEvidence, and MediSearch. These tools analyze complex data to aid clinical decision-making, enhanci...
AIM: This observational study aimed to verify and improve the predictive value of plaque microbiome of patients with dental implant for peri-implant diseases.
Oral implant surgery demands a high level of precision and expertise, making the integration of robotic assistance an optimal solution. This study introduces an innovative dental implant robotic system designed to enhance accuracy during cavity prepa...
OBJECTIVE: This study evaluates the performance of various classifiers and pre-trained models for dental implant state classification using preprocessed radiography images with masks.
The aim of this study was to evaluate the reliability and quality of information generated by ChatGPT regarding dental implants and peri-implant phenotypes. A structured questionnaire on these topics was presented to the AI-based chatbot, and its res...
With the ongoing advancement of digital technology, oral medicine transitions from traditional diagnostics to computer-assisted diagnosis and treatment. Identifying dental implants in patients without records is complex and time-consuming. Accurate i...
OBJECTIVES: Class imbalance in datasets is one of the challenges of machine learning (ML) in medical image analysis. We employed synthetic data to overcome class imbalance when segmenting bitewing radiographs as an exemplary task for using ML.
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