To determine the comparative accuracy of seven generative artificial intelligence (GenAI) platforms in answering multiple-choice questions on a predoctoral pediatric dentistry examination. This study evaluated the impact of question type and GenAI t...
To assess the diagnostic and treatment decision-making accuracy of ChatGPT for various dental problems in pediatric patients compared to specialized pediatric dentists. This study included 12 cases, each with an average of three dental problems, re...
To conduct a systematic review of artificial intelligence (AI) in aiding clinicians with the prediction and detection specifically for early childhood caries (ECC). A search was performed across PubMed, Scopus, Web of Science, IEEE, and grey litera...
To evaluate the accuracy and consistency of chatbots in answering questions related to special needs dentistry. Nine publicly accessible chatbots, including Google Bard, ChatGPT 4, ChatGPT 3.5, Llama, Sage, Claude 2 100k, Claude-instant, Claude-ins...
To develop a no-code artificial intelligence (AI) model capable of identifying primary proximal surface caries using bitewings among pediatric patients. One hundred bitewing radiographs acquired at pediatric dental clinics were anonymized and revie...
To systematically evaluate artificial intelligence applications for diagnostic and treatment planning possibilities in pediatric dentistry. PubMed, EMBASE, Scopus, Web of Science, IEEE, medRxiv, arXiv, and Google Scholar were searched using specifi...