Trial reportFrontiers in public health2026
Application of AI-based virtual standardized patients in physician-patient communication training: a study based on the SEGUE framework.
Trial report in Frontiers in public health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled 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.
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
2 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial intelligence for affective-domain development in healthcare professions education: a systematic review.Frontiers in medicine · 2026Pooled it
- Effect of Large Language Model-Powered Virtual Standardized Patients on History-Taking Among Undergraduate Medical Students: Propensity-Matched Cohort Study.JMIR medical education · 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
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Developing effective doctor-patient communication skills is a critical component of medical education. The SEGUE framework offers a structured and systematic approach for teaching and assessing communication competence. However, traditional standardized patient (SP) training is resource-intensive, time-consuming, and difficult to scale for large student cohorts. AI-based virtual standardized patients (AI-VSPs) have emerged as a promising alternative, providing repeatable, accessible, and scalable training opportunities. This study aimed to evaluate the effectiveness and feasibility of AI-VSPs within SEGUE-based communication training for medical students, compared with traditional SPs. Methods: In a parallel mixed-methods randomized controlled trial, 82 senior clinical medical students were randomized to train with AI-VSPs ( Results: Both groups improved significantly in SEGUE scores after training ( Conclusion: SEGUE-based simulation training effectively enhances medical students' communication skills. AI-VSPs offer scalable, repeatable, and practical advantages, helping learners complete communication tasks thoroughly and providing targeted feedback, but they do not necessarily improve real-world communication competence such as empathy or relational skills.
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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.