Evidence map›Paper›PMID 41563693›Full record

ArticleJournal of imaging informatics in medicine2026

Evaluation of ChatGPT-4 and Microsoft Copilot for Third-Molar Assessment on Panoramic Radiographs.

Thaísa Pinheiro Silva, Maria Fernanda Silva Andrade-Bortoletto, Caio Alencar-Palha, Thaís Santos Cerqueira Ocampo, Christiano Oliveira-Santos, Deborah Queiroz Freitas, Matheus L Oliveira

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In one paragraph

Article in Journal of imaging informatics in medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

7 authors.

Thaísa Pinheiro SilvaDepartment of Oral Diagnosis, Division of Oral Radiology, Piracicaba Dental School, University of Campinas, Zip Code, Piracicaba, Sao Paulo, 13414-903, Brazil.ORCID http://orcid.org/0000-0002-7485-0206
Maria Fernanda Silva Andrade-BortolettoDepartment of Oral Diagnosis, Division of Oral Radiology, Piracicaba Dental School, University of Campinas, Zip Code, Piracicaba, Sao Paulo, 13414-903, Brazil.
Caio Alencar-PalhaDepartment of Oral Diagnosis, Division of Oral Radiology, Piracicaba Dental School, University of Campinas, Zip Code, Piracicaba, Sao Paulo, 13414-903, Brazil.
Thaís Santos Cerqueira OcampoDepartment of Oral Diagnosis, Division of Oral Radiology, Piracicaba Dental School, University of Campinas, Zip Code, Piracicaba, Sao Paulo, 13414-903, Brazil.
Christiano Oliveira-SantosDepartment of Diagnosis and Oral Health, University of Louisville School of Dentistry, Louisville, KY, 40202, USA.
Deborah Queiroz FreitasDepartment of Oral Diagnosis, Division of Oral Radiology, Piracicaba Dental School, University of Campinas, Zip Code, Piracicaba, Sao Paulo, 13414-903, Brazil.
Matheus L OliveiraDepartment of Oral Diagnosis, Division of Oral Radiology, Piracicaba Dental School, University of Campinas, Zip Code, Piracicaba, Sao Paulo, 13414-903, Brazil. matheuso@unicamp.br.

Funding

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior 001
6 · The paper itself

Abstract

To assess the performance of two AI chatbot assistants in identifying the presence and classifying the position of third molars on panoramic radiographs. A total of 114 third molars from 100 panoramic radiographs were evaluated consensually by three examiners and independently by two AI chatbot assistants (ChatGPT-4 and Microsoft Copilot). They were asked to provide descriptions regarding the orientation of the third molar's long axis, level of bone inclusion, space between the lower second molar and the mandibular ramus, and proximity of the third molar to the mandibular canal or maxillary sinus. Keywords generated by the AI chatbot assistants were compared to those used by the examiners and scored as 0 (incorrect), 0.5 (partially correct), or 1 (correct). Mean scores and standard deviations were calculated for each parameter and compared using the Wilcoxon test (α = 0.05). Mean scores across the four parameters ranged from 0.08 to 0.30 (SD = 0.42-0.44) for ChatGPT-4 and from 0.25 to 0.31 (SD = 0.42-0.47) for Microsoft Copilot. The only significant difference in performance between the AI chatbots was observed in the space between the lower second molar and ramus, in favor of Microsoft Copilot (p < 0.05). Overall performance scores were 0.22 (SD = 0.42) for ChatGPT-4 and 0.28 (SD = 0.46) for Microsoft Copilot. Furthermore, hallucinations such as classifying absent teeth were also observed. Both ChatGPT-4 and Microsoft Copilot demonstrate generally low performance in accurately identifying and classifying the position of third molars on panoramic radiographs.

Indexed as

Artificial intelligenceGenerative pre-trained transformerMolarPanoramic radiographyThird

Identifiers

PMID41563693

What OpenQuestion holds

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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.