Evidence map›Paper›PMID 42799800›Full record

ArticleClinical oral investigations2026

Can vision-language models estimate patient age? evidence from panoramic radiographs.

Thaísa Pinheiro Silva, Fernanda Bulhões Fagundes, Maria Fernanda Andrade-Bortoletto, Christiano Oliveira-Santos, Deborah Queiroz Freitas, Matheus L Oliveira

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Article in Clinical oral investigations, 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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1 · What the graph read from it

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Thaísa Pinheiro SilvaDepartment of Oral Diagnosis, Division of Oral Radiology, Piracicaba Dental School, University of Campinas (UNICAMP), Piracicaba, Sao Paulo, 13414-903, Brazil. thaisapinheirosilva@hotmail.com.ORCID http://orcid.org/0000-0002-7485-0206
Fernanda Bulhões FagundesDepartment of Oral Diagnosis, Division of Oral Radiology, Piracicaba Dental School, University of Campinas (UNICAMP), Piracicaba, Sao Paulo, 13414-903, Brazil.ORCID http://orcid.org/0000-0002-9777-4197
Maria Fernanda Andrade-BortolettoDepartment of Oral Diagnosis, Division of Oral Radiology, Piracicaba Dental School, University of Campinas (UNICAMP), Piracicaba, Sao Paulo, 13414-903, Brazil.ORCID http://orcid.org/0000-0003-2300-3143
Christiano Oliveira-SantosDepartment of Diagnosis and Oral Health, University of Louisville School of Dentistry, Louisville, KY, 40202, USA.ORCID http://orcid.org/0000-0001-9936-7547
Deborah Queiroz FreitasDepartment of Oral Diagnosis, Division of Oral Radiology, Piracicaba Dental School, University of Campinas (UNICAMP), Piracicaba, Sao Paulo, 13414-903, Brazil.ORCID http://orcid.org/0000-0002-1425-5966
Matheus L OliveiraDepartment of Oral Diagnosis, Division of Oral Radiology, Piracicaba Dental School, University of Campinas (UNICAMP), Piracicaba, Sao Paulo, 13414-903, Brazil. matheuso@unicamp.br.

Funding

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior 0001
6 · The paper itself

Abstract

objectiveTo explore the performance and limitations of three vision-language models (VLMs) in estimating chronological age from panoramic radiographs. MATERIALS AND

methodsA sample of 140 panoramic radiographs from individuals aged 7 to 20 years was uploaded to three VLMs: ChatGPT-4o, Microsoft Copilot Fast, and Microsoft Copilot Deep. Each VLM was independently prompted to estimate chronological age based on dental eruption patterns. The results were compared with the individuals' chronological ages. Statistical analysis included agreement within ± 1 year percentage calculation, and one-way analysis of variance to compare estimated and chronological ages (α = 0.05).

resultsAll three VLMs demonstrated low agreement in age estimation, with overall performance below 35%. Slightly better agreement was observed in individuals aged 9-10 years. The models showed greater deviations from chronological age at the extremes of the age range (7-8 and 17-20 years). No correct estimations were recorded for individuals aged 7, 17, 18, 19, and 20 years for ChatGPT-4o, 15 and 20 years for Microsoft Copilot Fast, and 20 years for Microsoft Copilot Deep.

conclusionsThe evaluated VLMs showed limited agreement in estimating chronological age from panoramic radiographs. Therefore, their application in clinical and forensic contexts cannot currently be recommended. CLINICAL RELEVANCE: Understanding the potential applications and limitations of VLMs as an auxiliary tool in image interpretation is imperative to its use in clinical scenarios.

Indexed as

Age Determination by TeethRadiography, PanoramicAdolescentChildFemaleHumansMaleYoung AdultAge EstimationArtificial IntelligenceForensic OdontologyPanoramic RadiographsRadiologyVision-Language Models

Identifiers

PMID42799800
PMCPMC13616011

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