The goal of this prospective observational study is to evaluate the ability of three large language models (ChatGPT-4o, Gemini Advanced, and Claude 3.7) to support diagnosis and treatment decision-making in adult patients presenting with common endodontic conditions. The main questions the study aims to answer are: Can LLMs accurately determine the endodontic diagnosis when provided with structured clinical information and periapical radiographs? Can LLMs propose appropriate treatment plans comparable to decisions made by endodontic specialists? To answer these questions, researchers will compare the diagnostic and treatment accuracy of three AI models using a consensus diagnosis from endodontic specialists as the reference standard. Participants will: Receive routine endodontic examination and periapical radiographs as part of standard clinical care. Have their anonymized clinical histories and radiographs entered into the three AI models. Not interact directly with any AI system; all evaluations will be performed by the research team. This study aims to understand how large language models perform under real-world clinical conditions and whether these systems may play a supportive role in endodontic diagnostics in the future.
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Clinician Diagnosis Accuracy Based on Paper-Based History and Periapical Radiograph
Timeframe: 7 july-5 august