ArticleJMIR medical informatics2021
A Natural Language Processing-Based Virtual Patient Simulator and Intelligent Tutoring System for the Clinical Diagnostic Process: Simulator Development and Case Study.
Article in JMIR medical informatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 1 of them a synthesis that pooled it.
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Who cites it
24 citing papers in PubMed, 1 synthesis or guideline pooled it, 58 citations in OpenAlex.
- A systematic review of the impact of artificial intelligence on educational outcomes in health professions education.BMC medical education · 2025Pooled it
- Web-Based AI-Driven Virtual Patient Simulator Versus Actor-Based Simulation for Teaching Consultation Skills: Multicenter Randomized Crossover Study.JMIR formative research · 2025Trial
- Simulator development using natural language: clinician-led innovation through artificial intelligence.Advances in simulation (London, England) · 2026Review
- Development and pilot evaluation of a large language model-based clinical simulation system for medical education in gastroenterology.BMC medical education · 2026Article
- AI-generated Feedback Following Social Robotic Virtual Patient Interactions and Medical Student Performance: Nonrandomized Quasi-Experimental Study.JMIR medical education · 2026Article
- Use of generative large language models for patient education on common surgical conditions: a comparative analysis between ChatGPT and Google Gemini.Updates in surgery · 2026Article
- Artificial intelligence in medicine: a position paper by the Italian Society of Internal Medicine.Internal and emergency medicine · 2026Article
- Artificial intelligence in anesthesiology education: transformative applications, challenges, and future perspectives.Frontiers in medicine · 2026Review
- Virtual reality simulation to teach communication for undergraduate nursing students: a scoping review.BMC medical education · 2025Article
- Artificial intelligence in undergraduate medical education: an updated scoping review.BMC medical education · 2025Article
- Enhancing Patient-Physician Communication: Simulating African American Vernacular English in Medical Diagnostics with Large Language Models.Journal of healthcare informatics research · 2025Article
- A virtual patient authoring tool for transcatheter aortic valve replacement.International journal of computer assisted radiology and surgery · 2025Article
- Knowledge-point classification using simple LSTM-based and siamese-based networks for virtual patient simulation.BMC medical informatics and decision making · 2025Article
- Article
- Will Artificial Intelligence Be "Better" Than Humans in the Management of Syncope?JACC. Advances · 2024Review
- Strategies for Digital Clinical Teaching During the COVID Pandemic: A Scoping Review.Medical science educator · 2024Article
- Natural language processing for automatic evaluation of free-text answers - a feasibility study based on the European Diploma in Radiology examination.Insights into imaging · 2023Article
- Artificial Intelligence Supporting the Training of Communication Skills in the Education of Health Care Professions: Scoping Review.Journal of medical Internet research · 2023Article
- IvCDS: An End-to-End Driver Simulator for Personal In-Vehicle Conversational Assistant.International journal of environmental research and public health · 2022Article
- Intelligent virtual case learning system based on real medical records and natural language processing.BMC medical informatics and decision making · 2022Article
Corrections and comments
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Authors and funding
9 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundShortage of human resources, increasing educational costs, and the need to keep social distances in response to the COVID-19 worldwide outbreak have prompted the necessity of clinical training methods designed for distance learning. Virtual patient simulators (VPSs) may partially meet these needs. Natural language processing (NLP) and intelligent tutoring systems (ITSs) may further enhance the educational impact of these simulators.
objectiveThe goal of this study was to develop a VPS for clinical diagnostic reasoning that integrates interaction in natural language and an ITS. We also aimed to provide preliminary results of a short-term learning test administered on undergraduate students after use of the simulator.
methodsWe trained a Siamese long short-term memory network for anamnesis and NLP algorithms combined with Systematized Nomenclature of Medicine (SNOMED) ontology for diagnostic hypothesis generation. The ITS was structured on the concepts of knowledge, assessment, and learner models. To assess short-term learning changes, 15 undergraduate medical students underwent two identical tests, composed of multiple-choice questions, before and after performing a simulation by the virtual simulator. The test was made up of 22 questions; 11 of these were core questions that were specifically designed to evaluate clinical knowledge related to the simulated case.
resultsWe developed a VPS called Hepius that allows students to gather clinical information from the patient's medical history, physical exam, and investigations and allows them to formulate a differential diagnosis by using natural language. Hepius is also an ITS that provides real-time step-by-step feedback to the student and suggests specific topics the student has to review to fill in potential knowledge gaps. Results from the short-term learning test showed an increase in both mean test score (P<.001) and mean score for core questions (P<.001) when comparing presimulation and postsimulation performance.
conclusionsBy combining ITS and NLP technologies, Hepius may provide medical undergraduate students with a learning tool for training them in diagnostic reasoning. This may be particularly useful in a setting where students have restricted access to clinical wards, as is happening during the COVID-19 pandemic in many countries worldwide.
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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.