ArticleJournal of dental education2026
Generative Artificial Intelligence-Driven Clinical Case Simulation in Temporomandibular Disorder Education: ChatGPT Versus Real Patients.
Article in Journal of dental education, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.
What it found
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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.
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Who cites it
5 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Effectiveness of artificial intelligence-driven simulation in dental education: a systematic review and meta-analysis of learning outcomes.BMC medical education · 2026Pooled it
- Artificially Intelligent Simulated Patients in Undergraduate Surgical Teaching: A Single Blinded Randomized Controlled Study.The clinical teacher · 2026Trial
- Integrating virtual patient-based learning into temporomandibular disorder education improves educational and behavioral outcomes.BMC medical education · 2026Trial
- Generative Artificial Intelligence-Driven Clinical Case Simulation in Temporomandibular Disorder Education: ChatGPT Versus Real Patients.Journal of dental education · 2026Article
- Digital intelligence in dental technology education: curriculum and practical teaching reform.Frontiers in medicine · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
backgroundTemporomandibular disorders (TMDs) and orofacial pain (OFP) demand advanced diagnostic and clinical reasoning skills in dental education. Traditional simulations with real patients face limitations in availability and standardization. Generative artificial intelligence (GAI), such as ChatGPT-3.5, has emerged as a potential tool for clinical training.
methodsThis blinded, cross-sectional, crossover study involved 30 undergraduate dental students, each completing two simulated cases: one with ChatGPT-3.5 and one with standardized real patients. Cases were developed and validated by TMD/OFP specialists via the Delphi method and incorporated into the AI through structured prompts. Quantitative parameters (such as responses, word count, follow-up questions, reformulations, and diagnostic accuracy) and qualitative aspects (such as empathy, clarity, and relevant information) were analyzed.
resultsGAI simulations provided higher information density (231 vs.167 relevant units; p < 0.001) and clearer reasoning flow. Students interacting with real patients asked more follow-up questions (p = 0.004) and required more reformulations (p = 0.011), indicating more adaptive communication. Diagnostic accuracy did not differ significantly (p > 0.05). Relevant information correlated positively with diagnostic accuracy (r = 0.484; p = 0.007), whereas total word count correlated negatively (r = -0.386; p = 0.035).
conclusionsChatGPT-3.5 matched real patient simulations in diagnostic reasoning for TMD/OFP. Combining GAI's scalability and standardization with real patient variability may optimize clinical competency training.
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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.