ArticleCureus2024
Can Natural Language Processing (NLP) Provide Consultancy to Patients About Edentulism Teeth Treatment?
Article in Cureus, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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.
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.
Who cites it
1 citing paper in PubMed.
- Large language models in implant dentistry: a scoping review of applications, performance, and limitations.BMC oral health · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Aim This study aimed to evaluate the accuracy and quality of the answers given by artificial intelligence (AI) applications to the questions directed at tooth deficiency treatments. Materials and methods Fifteen questions asked by patients/ordinary people about missing tooth treatment were selected from the Quora platform. Questions were asked to the ChatGPT-4 (OpenAI Inc., San Francisco, California, United States) and Copilot (Microsoft Corporation, Redmond, Washington, United States) models. Responses were assessed by two expert physicians using a five-point Likert scale (LS) for accuracy and the Global Quality Scale (GQS) for quality. To assess the internal consistency and inter-rater agreement of ChatGPT-4 and Copilot, Cronbach's alpha, Spearman-Brown's coefficient, and Guttman's split-half coefficient were calculated to measure the reliability and internal consistency of both instruments (α=0.05). Results Copilot showed a mean LS value of 3.83±0.36 and ChatGPT-4 showed a lower mean value of 3.93±0.32. ChatGPT-4's GQS mean value (3.9±0.28) is also higher than Copilot (3.83±0.06) (p<0.001). Conclusion It can be said that AI chatbots gave highly accurate and consistent answers to questions about the treatment of toothlessness. With the ever-developing technology, AI chatbots can be used as consultants for dental treatments in the future.
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Registered trials
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.