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ArticleOral radiology2026

Artificial intelligence-assisted decision-making in third molar assessment using ChatGPT: is it really a valid tool?

Nadav Grinberg, Clariel Ianculovici, Sara Whitefield, Shlomi Kleinman, Svetlana Feldman, Oren Peleg

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Article in Oral radiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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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, Grey School of Medicine, Tel Aviv University, 64239, Tel Aviv, Israel. Nadav.grinberg@mail.huji.ac.il.ORCID 0009-0001-6347-9106
Clariel IanculoviciDepartment of Otolaryngology Head and Neck Surgery and Maxillofacial Surgery, Tel-Aviv Sourasky Medical Center, Grey School of Medicine, Tel Aviv University, 64239, Tel Aviv, Israel.
Sara WhitefieldDepartment of Otolaryngology Head and Neck Surgery and Maxillofacial Surgery, Oral Medicine Unit, Tel-Aviv Sourasky Medical Center, Tel Aviv, Israel.
Shlomi KleinmanDepartment of Otolaryngology Head and Neck Surgery and Maxillofacial Surgery, Tel-Aviv Sourasky Medical Center, Grey School of Medicine, Tel Aviv University, 64239, Tel Aviv, Israel.
Svetlana FeldmanDepartment of Otolaryngology Head and Neck Surgery and Maxillofacial Surgery, Oral Medicine Unit, Tel-Aviv Sourasky Medical Center, Tel Aviv, Israel.
Oren PelegDepartment of Otolaryngology Head and Neck Surgery and Maxillofacial Surgery, Tel-Aviv Sourasky Medical Center, Grey School of Medicine, Tel Aviv University, 64239, Tel Aviv, Israel.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesArtificial intelligence (AI) is becoming increasingly popular in medicine. The current study aims to investigate whether an AI-based chatbot, such as ChatGPT, could be a valid tool for assisting in decision-making when assessing mandibular third molars before extractions.

methodsPanoramic radiographs were collected from a publicly available library. Mandibular third molars were assessed by position and depth. Two specialists evaluated each case regarding the need for CBCT referral, followed by introducing all cases to ChatGPT under a uniform script to decide the need for further CBCT radiographs. The process was performed first without any guidelines, Second, after introducing the guidelines presented by Rood et al. (1990), and third, with additional test cases. ChatGPT and a specialist's decision were compared and analyzed using Cohen's kappa test and the Cochrane-Mantel--Haenszel test to consider the effect of different tooth positions. All analyses were made under a 95% confidence level.

resultsThe study evaluated 184 molars. Without any guidelines, ChatGPT correlated with the specialist in 49% of cases, with no statistically significant agreement (kappa < 0.1), followed by 70% and 91% with moderate (kappa = 0.39) and near-perfect (kappa = 0.81) agreement, respectively, after the second and third rounds (p < 0.05). The high correlation between the specialist and the chatbot was preserved when analyzed by the different tooth locations and positions (p < 0.01).

conclusionChatGPT has shown the ability to analyze third molars prior to surgical interventions using accepted guidelines with substantial correlation to specialists.

Indexed as

Artificial IntelligenceClinical Decision-MakingMolar, ThirdAdultCone-Beam Computed TomographyFemaleGenerative Artificial IntelligenceHumansMaleRadiography, PanoramicTooth ExtractionArtificial IntelligenceChatGPTCone-beam computed tomographyOral radiologyThird molar extraction

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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.