Endodontic Diagnosis, Endodontic Diseases, Endodontic Treatment, Endodontic Decision-makingNCT07281066
LLM Performance in Endodontic Diagnostics
An observational study of Endodontic Diagnosis, Endodontic Diseases, Endodontic Treatment, Endodontic Decision-making, run by Marmara University at 1 site in Turkey (Türkiye).
Trial summary
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. Read more about this study
- Who is it for?
- Ages 18 to 65 years
- Conditions the study is for
- Endodontic Diagnosis, Endodontic Diseases, Endodontic Treatment, Endodontic Decision-making
- What is being tested?
- AI-Based Diagnostic Assessment
- How many people
- 120 participants
- Where
- 1 siteTurkey (Türkiye)
- Study dates
- 7 Jul 2025to 3 Oct 2025
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 to 65 years
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: The study population consisted of adult patients attending or referred to the endodontic clinic of Marmara University. All participants presented with common endodontic conditions such as pulpitis, necrosis, primary or secondary apical periodontitis, or the need for retreatment. After obtaining consent, each patient underwent a structured paper-based medical and dental history assessment and periapical radiographic examination. A total of 120 clinically verified endodontic cases were included.
Who may be able to join
- 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.
Who cannot join
- 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.
Trial locations and contacts
1 site in 1 country.
Turkey (Türkiye)1 site
Faculty of Dentistry, Marmara University
Maltepe, Istanbul, 34856
About this study
Official title: Evaluating ChatGPT-4o, Gemini and Claude 3.7 in Endodontic Diagnostics: A Prospective Clinical Study
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:
Read more
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.
Read the detailed description
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.
- Conditions
- Endodontic Diagnosis, Endodontic Diseases, Endodontic Treatment, Endodontic Decision-making
- Being tested
- AI-Based Diagnostic Assessment
- Keywords
- access cavity cleaning, air abrasion, air polishing, CLSM, ethanol, push-out bond strength
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
AI-Based Diagnostic AssessmentDiagnostic 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.
| Study group | Role | What they receive |
|---|---|---|
| Endodontic Patients Cohort | This cohort includes 120 consecutive patients presenting to the endodontic clinic with clinically verified endodontic conditions. Clinical history and periapical radiographs were collected, and diagnostic/treatment recommendations generated by AI models were compared with expert consensus. Receives: Diagnostic Test: AI-Based Diagnostic Assessment |
What is being measured
The primary outcome is the main question the study is designed to answer.
Clinician Diagnosis Accuracy Based on Paper-Based History and Periapical Radiograph
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.
Measured: 7 july-5 august
Secondary outcomes (1)
LLM-Generated Diagnosis and Treatment Planning Performance
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.
Measured: august-september
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted15 Dec 2025
- Study start7 Jul 2025
- Primary completion5 Aug 2025
- Study completion3 Oct 2025
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (2)Papers the sponsor has linked to this study.
- Other trials for Endodontic Diagnosis, Endodontic Diseases, Endodontic Treatment, Endodontic Decision-makingStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Case only
- Time perspective
- Prospective
- Enrolment
- 120 participants (actual)
Sponsor and investigators
- Lead sponsor
- Marmara University
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
- Investigators
- ayşe karadayı, asst. prof., Study director, marmara university faculty of dentistry
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07281066
- Sponsor's protocol ID
- 2025-38
- Status last verified
- Dec 2025
- First submitted
- 24 Nov 2025
- Last updated
- 15 Dec 2025
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Undecided
Publications
Abd-Alrazaq A, AlSaad R, Alhuwail D, Ahmed A, Healy PM, Latifi S, Aziz S, Damseh R, Alabed Alrazak S, Sheikh J. Large Language Models in Medical Education: Opportunities, Challenges, and Future Directions. JMIR Med Educ. 2023 Jun 1;9:e48291. doi: 10.2196/48291.
PubMed 37261894Schwendicke F, Samek W, Krois J. Artificial Intelligence in Dentistry: Chances and Challenges. J Dent Res. 2020 Jul;99(7):769-774. doi: 10.1177/0022034520915714. Epub 2020 Apr 21.
PubMed 32315260
Study at a glance
From the registry record, last updated by the sponsor on 15 Dec 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Evaluating ChatGPT-4o, Gemini and Claude 3.7 in Endodontic Diagnostics: A Prospective Clinical Study
- ClinicalTrials.gov ID
- NCT07281066
- Acronym / sponsor ID
- 2025-38
- Conditions
- Endodontic Diagnosis, Endodontic Diseases, Endodontic Treatment, Endodontic Decision-making
- Keywords
- access cavity cleaning, air abrasion, air polishing, CLSM, ethanol, push-out bond strength
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 120 participants (actual)
- Lead sponsor
- Marmara UniversityUniversity, hospital or other
- Study start
- 7 Jul 2025 (actual)
- Primary completion
- 5 Aug 2025 (actual)
- Study completion
- 3 Oct 2025 (actual)
- First posted
- 15 Dec 2025 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Detailed description
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.
Study design
- Observational model
- Case only
- Time perspective
- Prospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Endodontic Patients Cohort | – | Diagnostic Test: AI-Based Diagnostic AssessmentThis cohort includes 120 consecutive patients presenting to the endodontic clinic with clinically verified endodontic conditions. Clinical history and periapical radiographs were collected, and diagnostic/treatment recommendations generated by AI models were compared with expert consensus. |
Who may qualify
- Ages
- 18 to 65 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- The study population consisted of adult patients attending or referred to the endodontic clinic of Marmara University. All participants presented with common endodontic conditions such as pulpitis, necrosis, primary or secondary apical periodontitis, or the need for retreatment. After obtaining consent, each patient underwent a structured paper-based medical and dental history assessment and periapical radiographic examination. A total of 120 clinically verified endodontic cases were included.
Key inclusion criteria (4 listed)
- 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.
Key exclusion criteria (5 listed)
- 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.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| AI-Based Diagnostic AssessmentParticipants' 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. | Diagnostic test | Endodontic Patients Cohort |
Primary outcome
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.
1 secondary outcome
LLM-Generated Diagnosis and Treatment Planning Performance
Time frame: august-september
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 15 Dec 2025.
- Results publication: PubMed 37261894Abd-Alrazaq A, AlSaad R, Alhuwail D, Ahmed A, Healy PM, Latifi S, Aziz S, Damseh R, Alabed Alrazak S, Sheikh J. Large Language Models in Medical Education: Opportunities, Challenges, and Future Directions. JMIR Med Educ. 2023 Jun 1;9:e48291. doi: 10.2196/48291.
- Results publication: PubMed 32315260Schwendicke F, Samek W, Krois J. Artificial Intelligence in Dentistry: Chances and Challenges. J Dent Res. 2020 Jul;99(7):769-774. doi: 10.1177/0022034520915714. Epub 2020 Apr 21.
- Individual participant dataSharing planned: Undecided
Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
ayşe karadayı, asst. prof. study director
marmara university faculty of dentistry
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Endodontic Diagnosis, Endodontic Diseases, Endodontic Treatment, Endodontic Decision-making, run by Marmara University at 1 site in Turkey (Türkiye).
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 4 inclusion and 5 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 15 Dec 2025. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Turkey (Türkiye). Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Clinician Diagnosis Accuracy Based on Paper-Based History and Periapical Radiograph (time frame: 7 july-5 august). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT07281066, which also lists 2 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 15 Dec 2025.