Evidence map›Paper›PMID 39723544›Full record

ArticleJournal of clinical periodontology2025

Development and Comparative Evaluation of a Reinstructed GPT-4o Model Specialized in Periodontology.

Francesco Fanelli, Muhammad Saleh, Pasquale Santamaria, Khrystyna Zhurakivska, Luigi Nibali, Giuseppe Troiano

Abstract readComparative Study
In one paragraph

Article in Journal of clinical periodontology, 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.

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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.

Francesco FanelliDepartment of Clinical and Experimental Medicine, University of Foggia, Foggia, Italy.
Muhammad SalehDepartment of Periodontics and Oral Medicine, University of Michigan School of Dentistry, Ann Arbor, Michigan, USA.ORCID 0000-0001-5067-7317
Pasquale SantamariaCentre for Host Microbiome Interactions, Faculty of Dentistry, Oral and Craniofacial Sciences, King's College London, London, UK.ORCID 0000-0003-4102-1759
Khrystyna ZhurakivskaDepartment of Clinical and Experimental Medicine, University of Foggia, Foggia, Italy.
Luigi NibaliCentre for Host Microbiome Interactions, Faculty of Dentistry, Oral and Craniofacial Sciences, King's College London, London, UK.ORCID 0000-0002-7750-5010
Giuseppe TroianoDepartment of Clinical and Experimental Medicine, University of Foggia, Foggia, Italy.ORCID 0000-0001-5647-4414

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundArtificial intelligence (AI) has the potential to enhance healthcare practices, including periodontology, by improving diagnostics, treatment planning and patient care. This study introduces 'PerioGPT', a specialized AI model designed to provide up-to-date periodontal knowledge using GPT-4o and a novel retrieval-augmented generation (RAG) system.

methodsPerioGPT was evaluated in two phases. First, its performance was compared against those of five other chatbots using 50 periodontal questions from specialists, followed by a validation with 71 questions from the 2023-2024 'In-Service Examination' of the American Academy of Periodontology (AAP). The second phase focused on assessing PerioGPT's generative capacity, specifically its ability to create complex and accurate periodontal questions.

resultsPerioGPT outperformed other chatbots, achieving a higher accuracy rate (81.16%) and generating more complex and precise questions with a mean complexity score of 3.81 ± 0.965 and an accuracy score of 4.35 ± 0.898. These results demonstrate PerioGPT's potential as a leading tool for creating reliable clinical queries in periodontology.

conclusionsThis study underscores the transformative potential of AI in periodontology, illustrating that specialized models can offer significant advantages over general language models for both educational and clinical applications. The findings highlight that tailoring AI technologies to specific medical fields may improve performance and relevance.

Indexed as

Artificial IntelligencePeriodonticsHumansartificial intelligenceChatGPTclinical decision supportdental AIperiodontologyretrieval‐augmented generation

Identifiers

PMID39723544
PMCPMC12003056

What OpenQuestion holds

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LicenceCC BY-NC
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Registered trials

None linked

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.