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OpenTrials
Completed

NCT Number: NCT07281066

LLM Performance in Endodontic Diagnostics

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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Key information

Age range

18 year–65 year

Sex eligibility

All sexes

Study type

Observational

Primary location

Faculty of Dentistry, Marmara University

Maltepe, Istanbul, 34856, Turkey (Türkiye)

About this study

This prospective observational study aims to evaluate the real-time diagnostic and treatment decision-making performance of three large language models-ChatGPT-4o, Gemini Advanced, and Claude 3.7-in an endodontic clinical setting. A total of 120 patients presenting to the endodontic clinic were examined, and detailed medical/dental histories, clinical findings, and periapical radiographs were collected. Each anonymized case was then presented to the three LLMs using a standardized prompt asking for the diagnosis and the appropriate treatment plan.

All models were used in their default multimodal configurations without enabling web-search functions, plug-ins, or external data retrieval. Each question was submitted only once in isolated chat sessions to prevent memory carry-over. Responses were saved verbatim and compared with the reference diagnoses and treatment plans established by a panel of endodontic specialists.

This study was designed to mimic real-world clinical conditions as closely as possible, providing a realistic assessment of how these systems might perform when used by clinicians in everyday practice. Understanding their capabilities and limitations in authentic clinical scenarios is essential, as LLMs are expected to play an increasingly vital role in future dental care particularly in decision support, triage, and patient education. By identifying where these models perform well and where they fall short, this research aims to inform safe and effective clinical integration as LLM technologies continue to advance.

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

  • Adult patients (≥18 years old) presenting to or referred to the Endodontic Clinic.

Patients with a clinically verified endodontic condition requiring diagnosis and treatment planning.

Patients who agreed to participate and provided informed consent.

Patients for whom a complete paper-based medical/dental history and periapical radiograph were obtained during the clinical visit.

Exclusion criteria

  • Exclusion Criteria

Patients who declined participation or did not provide informed consent.

Pediatric patients (<18 years old) referred to the Pediatric Dentistry Clinic.

Patients attending the clinic with non-endodontic complaints (e.g., post-extraction alveolitis, third-molar extraction problems).

Cases with incomplete clinical information or missing radiographs.

Patients unable to undergo standard endodontic examination procedures.

Treatment and study plan

AI-Based Diagnostic Assessment

Diagnostic Test

Participants' anonymized clinical information, including structured patient history and periapical radiographs, was used as input for three large language models (ChatGPT-4o, Gemini Advanced, Claude 3.7). The models were asked to determine the endodontic diagnosis and propose an appropriate treatment plan. No treatment, device, or drug was administered to participants. The intervention consists solely of AI-based interpretation of pre-existing clinical data.

Primary outcomes

  1. Clinician Diagnosis Accuracy Based on Paper-Based History and Periapical Radiograph

    Time frame: 7 july-5 august

    Assessment of the diagnostic decision made by endodontic clinicians after reviewing a paper-based patient history form and a standardized periapical radiograph. Accuracy is determined by comparing the clinician's diagnosis with the consensus diagnosis established by three independent endodontic specialists. Data will be collected for all 120 patients at the time of initial clinical evaluation.

Secondary outcomes

  1. LLM-Generated Diagnosis and Treatment Planning Performance

    Time frame: august-september

    Evaluation of diagnostic and treatment recommendations generated by large language models (LLMs)-ChatGPT-4o, Gemini Advanced, and Claude 3.7-after receiving the same paper-based patient history and periapical radiograph provided to clinicians. LLM responses will be compared to the gold-standard specialist consensus for both diagnosis and treatment decisions.

Sponsors and collaborators

Lead sponsor

Marmara University

Other

Registry information

Official study title

Evaluating ChatGPT-4o, Gemini and Claude 3.7 in Endodontic Diagnostics: A Prospective Clinical Study

Important dates

Study start
2025
Primary completion
2025
Study completion
2025
First posted
Dec 15, 2025
Registry last updated
Dec 15, 2025

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.