CoChat
Sign Up
Cited by 52Artificial Intelligence in Healthcare and EducationInnovations in Medical EducationSimulation-Based Education in Healthcare

Intelligent virtual case learning system based on real medical records and natural language processing

Open access
Wang, Mengying, Sun, Zhen, Jia, Mo, Wang, Yan, Wang, Heng, Zhu, Xingxing, Chen, Lianzhong, Ji, Hong·2022·BMC Medical Informatics and Decision Making
DOI 10.1186/s12911-022-01797-7PMC PMC8895690
Open this paper in CoChat — free accountOpen PDF

Abstract

Background: Modernizing medical education by using artificial intelligence and other new technologies to improve the clinical thinking ability of medical students is an important research topic in recent years. Prominent medical universities are actively conducting research and exploration in this area. In particular, given the shortage of human resources, the need to maintain social distancing to prevent the spread of the epidemics, and the increase in the cost of medical education, it is critical to harness online learning to promote medical education. A virtual case learning system that uses natural language processing technology to process and present a hospital’s real medical records and evaluate student responses can effectively improve medical students’ clinical thinking abilities. Objective: The purpose of this study is to develop a virtual case system, AIteach, based on actual complete hospital medical records and natural language processing technology, and achieve clinical thinking ability improvement through a contactless, self-service, trial-and-error system application. Methods: Case extraction is performed on a hospital’s case data center and the best-matching cases are produced through natural language processing, word segmentation, synonym conversion, and sorting. A standard clinical questioning data module, virtual case data module, and student learning difficulty module are established to achieve simulation. Students can view the objective examination and inspection data of actual cases, including details of the consultation and physical examination, and automatically provide their learning response via a multi-dimensional evaluation system. In order to assess the changes in students’ clinical thinking after using AIteach, 15 medical graduate students were subjected to two simulation tests before and after learning through the virtual case system. The tests, which included the full-process case examination of cases having the same difficulty level, examined core clinical thinking test points such as consultation, physical examination, and disposal, and generated multi-dimensional evaluation indicators (rigor, logic, system, agility, and knowledge expansion). Thus, a complete and credible evaluation system is developed. Results: The AIteach system used an internal and external double-cycle learning model. Students collect case information through online inquiries, physical examinations, and other means, analyze the information for feedback verification, and generate their detailed multi-dimensional clinical thinking after learning. The feedback report can be evaluated and its knowledge gaps analyzed. Such learning based on real cases is in line with traditional methods of disease diagnosis and treatment, and addresses the practical difficulties in reflecting actual disease progression while keeping pace with recent research. Test results regarding short-term learning showed that the average score ( P < 0.01) increased from 69.87 to 85.6, the five indicators of clinical thinking evaluation improved, and there was obvious logical improvement, reaching 47%. Conclusion: By combining real cases and natural language processing technology, AIteach can provide medical students (including undergraduates and postgraduates) with an online learning tool for clinical thinking training. Virtual case learning helps students to cultivate clinical thinking abilities even in the absence of clinical tutor, such as during pandemics or natural disasters.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1186/s12911-022-01797-7

Cites

34 of 34 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • AI-powered educational tools can support adaptive learning, intelligent tutoring, automated feedback, and personalized learning pathways.

    “AI-powered educational tools can support adaptive learning, intelligent tutoring, automated feedback, and personalized learning pathways [8, 9].”

    From Effectiveness of a generative AI-powered digital tutor integrated with a knowledge graph in anatomy education for nursing students: a randomized controlled trial (2026) · cites this paper as [9]

    The Results section describes AIteach responding to students’ questions, checking diagnoses, scoring work, and marking missing diagnostic factors; the Conclusion says it supports learning without a clinical tutor. These passages support interactive tutoring and automated feedback, but they do not establish adaptive learning or personalized learning pathways. (LLM, conf=0.85)

    Experimental — accuracy not yet measured

  • AI-assisted educational systems can improve student engagement, provide adaptive learning support, and promote deeper understanding of complex concepts.

    “Previous research has also indicated that AI-assisted educational systems can improve student engagement, provide adaptive learning support, and promote deeper understanding of complex concepts [8, 9, 11].”

    From Effectiveness of a generative AI-powered digital tutor integrated with a knowledge graph in anatomy education for nursing students: a randomized controlled trial (2026) · cites this paper as [9]

    The Methods section ('Cultivating students’ clinical thinking with various difficulties'; page unavailable) describes difficulty levels students can choose to suit their knowledge and says the cases can improve understanding. The Results ('Short-term learning test results'; page unavailable) report improved clinical-thinking scores. These support tailored learning and improved understanding in medical training, but the paper does not establish improved student engagement or the broader claim about complex concepts across AI-assisted educational systems. (LLM, conf=0.86)

    Experimental — accuracy not yet measured

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

Research works printed but not linked: 1

  1. [6]Cunningham J, Hellmich C, Docherty SM, Lyall H, Collins A. Virtual morphology meetings - moving haematology online and across the County (Medical Education in the time of COVID-19 Virtual Conference) 2020. Virtual Morphology Meetings 2020.

Cited by

Sorted by citations

Related

Authors
Wang, M., Sun, Z., Jia, M., Wang, Y., Wang, H., Zhu, X., Chen, L., & Ji, H.
Affiliations
Peking University Third Hospital (CN)
Corresponding author
Ji, Hong
ORCID
Venue
BMC Medical Informatics and Decision Making
Publisher
Springer Science and Business Media LLC
Published
2022-03-04
Volume
22
Issue
1
Pages
60-60
ISSN
1472-6947 (ISSN-L)
DOI
10.1186/s12911-022-01797-7
PMC
PMC8895690
OpenAlex
W4214830647
Cited by
52
References
34
Citations by year
2026: 16 · 2025: 14 · 2024: 14 · 2023: 6 · 2022: 2
FWCI
2.71
Type
journal article
Language
en
Open access
Licence
Locations
  • BMC Medical Informatics and Decision Making (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • Peking University
  • Peking University Third Hospital
Topics
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
  • Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Innovations in Medical Education
  • Health Sciences › Medicine › Physiology › Simulation-Based Education in Healthcare
Keywords
medical education, clinical thinking ability, natural language processing, hospital medical records, student response evaluation, online learning, knowledge gaps, medical students, COVID-19 pandemic, word segmentation
MeSH terms
Artificial Intelligence, Humans, Learning, Medical Records, Natural Language Processing, Students, Medical

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review