Evidence map›Paper›PMID 40097790›Full record

ArticleClinical oral investigations2025

Assessing the performance of an artificial intelligence based chatbot in the differential diagnosis of oral mucosal lesions: clinical validation study.

Nadav Grinberg, Sara Whitefield, Shlomi Kleinman, Clariel Ianculovici, Gilad Wasserman, Oren Peleg

Abstract readValidation Study
PubMed Publisher
In one paragraph

Article in Clinical oral investigations, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

12 citing papers in PubMed.

  1. Article
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  7. Artificial Intelligence chatbots in oral pathology: a scoping review of applications, accuracy, and limitations.European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery · 2026
    Article
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  9. AI-assisted image analysis ofBioinformation · 2026
    Article
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  11. Performance of AI Chatbots in Preliminary Diagnosis of Maxillofacial Pathologies.Medical science monitor : international medical journal of experimental and clinical research · 2025
    Article
  12. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Nadav GrinbergDepartment of Otolaryngology, Head and Neck Surgery and Maxillofacial Surgery, Tel-Aviv Sourasky Medical Center, 64239, Tel Aviv, Israel. Nadav.grinberg@mail.huji.ac.il.
Sara WhitefieldOral Medicine Unit, Head and Neck Surgery and Maxillofacial Surgery, Tel-Aviv Sourasky Medical Center, 64239, Tel Aviv, Israel.
Shlomi KleinmanDepartment of Otolaryngology, Head and Neck Surgery and Maxillofacial Surgery, Tel-Aviv Sourasky Medical Center, 64239, Tel Aviv, Israel.
Clariel IanculoviciDepartment of Otolaryngology, Head and Neck Surgery and Maxillofacial Surgery, Tel-Aviv Sourasky Medical Center, 64239, Tel Aviv, Israel.
Gilad WassermanOral Medicine Unit, Head and Neck Surgery and Maxillofacial Surgery, Tel-Aviv Sourasky Medical Center, 64239, Tel Aviv, Israel.
Oren PelegDepartment of Otolaryngology, Head and Neck Surgery and Maxillofacial Surgery, Tel-Aviv Sourasky Medical Center, 64239, Tel Aviv, Israel.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesArtificial intelligence (AI) is becoming more popular in medicine. The current study aims to investigate, primarily, if an AI-based chatbot, such as ChatGPT, could be a valid tool for assisting in establishing a differential diagnosis of oral mucosal lesions.

methodsData was gathered from patients who were referred to our clinic for an oral mucosal biopsy by one oral medicine specialist. Clinical description, differential diagnoses, and final histopathologic diagnoses were retrospectively extracted from patient records. The lesion description was inputted into ChatGPT version 4.0 under a uniform script to generate three differential diagnoses. ChatGPT and an oral medicine specialist's differential diagnosis were compared to the final histopathologic diagnosis.

results100 oral soft tissue lesions were evaluated. A statistically significant correlation was found between the ability of the Chatbot and the Specialist to accurately diagnose the cases (P < 0.001). ChatGPT demonstrated remarkable sensitivity for diagnosing urgent cases, as none of the malignant lesions were missed by the chatbot. At the same time, the specificity of the specialist was higher in cases of malignant lesion diagnosis (p < 0.05). The chatbot performance was reliable in two different events (p < 0.01).

conclusionChatGPT-4 has shown the ability to pinpoint suspicious malignant lesions and suggest an adequate differential diagnosis for soft tissue lesions, in a consistent and repetitive manner. CLINICAL RELEVANCE: This study serves as a primary insight into the role of AI chatbots, as assisting tools in oral medicine and assesses their clinical capabilities.

Indexed as

Artificial IntelligenceMouth DiseasesMouth MucosaAdultAgedBiopsyDiagnosis, DifferentialFemaleGenerative Artificial IntelligenceHumansMaleMiddle AgedMouth NeoplasmsRetrospective StudiesSensitivity and SpecificityArtificial intelligenceChatGPTDifferential diagnosisOral mucosal lesionsOral squamous cell carcinoma

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.