Evidence map›Paper›PMID 42160812›Full record

ArticleInternational dental journal2026

Integration of Artificial Intelligence in Designing Removable Partial Dentures.

Omar Alageel

Abstract read
In one paragraph

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

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

1 author.

Omar AlageelDental Health Department, College of Applied Medical Sciences, King Saud University, Riyadh, Saudi Arabia. Electronic address: oalageel@ksu.edu.sa.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeThe integration of artificial intelligence (AI) into prosthodontics represents a paradigm shift in the design and fabrication of removable partial dentures (RPDs). This study evaluates the current AI technologies, including large language models such as ChatGPT, Copilot, Gemini, and DeepSeek, for designing RPDs.

methodsStandardized prompts were submitted to four AI-assisted systems (ChatGPT, Copilot, Gemini, and DeepSeek) to generate RPD designs for 25 partially edentulous clinical scenarios based on Kennedy classifications. The outputs were compared with reference models (AiDENTAL), a validated retention prediction model, and evaluations by six blinded experts. Statistical comparisons were performed using a mixed-effects ordinal regression model (p < 0.05).

resultsCompared with the reference standards, Copilot and Gemini produced the most consistent and accurate RPD designs, whereas DeepSeek showed the greatest deviation, despite achieving the highest predicted retention scores. Expert evaluations confirmed that AiDENTAL achieved the highest overall design quality, followed by Copilot, whereas ChatGPT and DeepSeek demonstrated lower and more variable performances.

conclusionsThe design of RPDs using different large language models, including ChatGPT, Copilot, Gemini, and DeepSeek, resulted in notable variations in the design output. AiDENTAL achieved the highest overall RPD design quality, whereas Copilot and Gemini produced the most consistent and accurate designs across all the evaluated criteria.

Indexed as

Artificial IntelligenceDenture DesignDenture, Partial, RemovableComputer-Aided DesignGenerative Artificial IntelligenceHumansIntelligent SystemsLarge Language ModelsArtificial intelligenceChatGPTCopilotDeepSeekGeminiProsthodonticsRemovable partial dentures

Identifiers

PMID42160812
PMCPMC13217586

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.