ReviewOdontology2026
Large language models and generative artificial intelligence in endodontics: a scoping review.
Review in Odontology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The aim of this study is to comprehensively examine the evolution of artificial intelligence (AI), specifically large language models (LLMs), in the field of endodontics under the headings of clinical decision support systems, trauma management, regenerative treatments, patient education, and academic performance. This scoping review was conducted in accordance with the PRISMA-ScR guidelines. A search was performed on March 9, 2026, across the PubMed and Scopus databases, screening original research articles published in English between January 1, 2020, and March 9, 2026. A total of 54 studies involving the applications of LLMs and generative AI (GenAI) in the field of endodontics were included in the qualitative synthesis. The studies demonstrated that LLMs (specifically ChatGPT-4o, ChatGPT-5.1, ChatGPT-5 Plus, Gemini 2.5 Pro/Flash, Claude 3.5/3.7, and DeepSeek-R1) compete with specialist clinicians in standard theoretical and clinical scenarios, even achieving equivalent alignment in certain cases; however, their performance declines in complex cases. It has been determined that the Retrieval-Augmented Generation (RAG) architecture significantly reduces the risk of 'hallucination' by integrating current clinical guidelines to make the system document-grounded. However, critical medico-legal challenges and ethical constraints persist, such as data confidentiality, algorithmic biases, malpractice accountability, and the necessity of informed consent. While artificial intelligence holds transformative potential in endodontics, it must not be regarded as an autonomous decision-maker. To mitigate risks like 'automation bias' and potential deskilling, AI should be positioned as an 'assistive clinical adjutant' that operates in seamless synergy with human proficiency, ultimately augmenting the clinician's cognitive filtration and legal responsibility.
Indexed as
Identifiers
42265501What OpenQuestion holds
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.