ArticleMedical education online2023
Designing and building OSCEBot ® for virtual OSCE - Performance evaluation.
Article in Medical education online, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 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.
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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
17 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The impact of generative AI on health professional education: A systematic review in the context of student learning.Medical education · 2025Pooled it
- Comparing AI chatbot simulation and peer role-play for OSCE preparation: a pilot randomized controlled trial.BMC medical education · 2025Trial
- Feasibility of AI and Human Standardized Patients to Enhance Customer Discovery Communication Skills in Medical Students: Preliminary Evaluation of an Observational Cohort Study.JMIR formative research · 2026Observational
- Agreement Between Reasoning-Oriented Generative AI Models and Clinical Educators in Evaluating Japanese Objective Structured Clinical Examination Transcripts: Preliminary Comparative Study.JMIR formative research · 2026Article
- Endodontic diagnostics training in undergraduate dental education: An observational pilot study on AI-driven virtual patient e-learning.International endodontic journal · 2026Observational
- Enhancing history-taking education through GPT-4-based virtual patients and automated assessment: a study of medical student perceptions.Korean journal of medical education · 2026Article
- Artificial Intelligence in Medical Education: Transformative Potential, Current Applications, and Future Implications.JMIR medical education · 2026Article
- AI-Driven Objective Structured Clinical Examination Generation in Digital Health Education: Comparative Analysis of Three GPT-4o Configurations.JMIR medical education · 2026Article
- Large language model-generated versus teacher-written objective structured clinical examination stations for medical students: a blinded comparative pilot study.Journal of educational evaluation for health professions · 2026Article
- What and when: a Delphi study identifying AI competencies and learning objectives for medical students.Frontiers in digital health · 2026Article
- The Validity of Generative Artificial Intelligence in Evaluating Medical Students in Objective Structured Clinical Examination: Experimental Study.JMIR formative research · 2025Article
- Artificial intelligence in undergraduate medical education: an updated scoping review.BMC medical education · 2025Article
- Exploring prospects, hurdles, and road ahead for generative artificial intelligence in orthopedic education and training.BMC medical education · 2024Review
- Cost management analysis of Objective Structured Clinical Examination (OSCE): guide to the universities of medical sciences.BMC medical education · 2024Article
- Roles, Users, Benefits, and Limitations of Chatbots in Health Care: Rapid Review.Journal of medical Internet research · 2024Review
- Education and Training Assessment and Artificial Intelligence. A Pragmatic Guide for Educators.British journal of biomedical science · 2024Review
- Artificial Intelligence and Objective Structured Clinical Examinations: Using ChatGPT to Revolutionize Clinical Skills Assessment in Medical Education.Journal of medical education and curricular developmentArticle
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
With AI's advancing technology and chatbots becoming more intertwined in our daily lives, pedagogical challenges are occurring. While chatbots can be used in various disciplines, they play a particularly significant role in medical education. We present the development process of OSCEBot ®, a chatbot to train medical students in the clinical interview approach. The SentenceTransformers, or SBERT, framework was used to develop this chatbot. To enable semantic search for various phrases, SBERT uses siamese and triplet networks to build sentence embeddings for each sentence that can then be compared using a cosine-similarity. Three clinical cases were developed using symptoms that followed the SOCRATES approach. The optimal cutoffs were determined, and each case's performance metrics were calculated. Each question was divided into different categories based on their content. Regarding the performance between cases, case 3 presented higher average confidence values, explained by the continuous improvement of the cases following the feedback acquired after the sessions with the students. When evaluating performance between categories, it was found that the mean confidence values were highest for previous medical history. It is anticipated that the results can be improved upon since this study was conducted early in the chatbot deployment process. More clinical scenarios must be created to broaden the options available to students.
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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.