ReviewFrontiers in medicine2026
AI-enabled language technologies for language-mediated learning and clinical communication in international undergraduate dental education: a scoping review.
Review in Frontiers in medicine, 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
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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.
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Authors and funding
4 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Background: International undergraduate dental students often learn and communicate in a language other than their first language, affecting terminology acquisition, clinical reasoning, patient explanations, informed consent, clinical communication, and patient safety. AI-enabled language technologies-including generative artificial intelligence (GenAI), large language models (LLMs), and neural machine translation (NMT)-may support these tasks, but their use in this population has not been systematically mapped. Methods: Following the Arksey and O'Malley framework, JBI guidance, and PRISMA-ScR, PubMed, Scopus, and Web of Science were searched from inception. Searches were conducted on 1 March 2026 and updated on 28 March 2026, supplemented by reference-list screening and targeted policy and grey-literature searches. Sources were charted by context, population, technology, outcomes, limitations, risks, and implementation, and classified by relevance. Results: Thirty-six non-policy sources and four policy or governance documents were included. Five involved undergraduate dental students, three addressed international, multilingual, or limited-English-proficiency learners or relevant policies, eight concerned other health-professions contexts, and eight were translation or technical benchmarks without learners. Categories overlapped, and only one source combined undergraduate dental students, an international or multilingual population, and an AI-enabled language technology. Applications included terminology translation, multilingual tutoring, communication rehearsal, and reflective-writing support. No source assessed retained learning, transfer to authentic clinical encounters, or patient-level outcomes. Conclusions: AI-enabled language technologies may support supervised language learning and patient-safety-oriented communication training, but direct evidence is sparse, short term, and largely derived from adjacent populations or technical benchmarks. Performance varies by language, task, prompt, model version, and context. Pending direct, comparative, longitudinal, and clinically situated evidence, implementation should include validated local resources, educator oversight, academic-integrity boundaries, privacy safeguards, and staged rehearsal before patient exposure.
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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.