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An Intelligent Virtual Standard Patient for Medical Students Training Based on Oral Knowledge Graph
Abstract
Virtual standard patient (VSP) is in high demand for medical students' diagnosis ability training in an efficient manner. Different from the traditional conversation system in medical dialogue generation, VSP needs a novel conversation paradigm to act as the patient instead of the doctor. However, existing conversation techniques still have limited ability in terms of generation of symptoms exhibited by patients with the personalized and knowledge-centered expressions. To alleviate these problems, we propose to construct a novel oral knowledge graph, which sufficiently provides medical clues of the certain disease. Accordingly, the VSP could accurately interact with the dentists for their underlying intention and express the symptoms characters in a natural style. To efficiently retrieve the related disease clues, the symptoms descriptions of the oral diseases are encoded into the oral knowledge graph, which could well organize the disease-centered symptom entities and speaking styles. Moreover, to transfer the common sense knowledge from existing large scale of medical knowledge graph to the specific oral knowledge graph, a coupled pre-trained Bert models is further designed to learn the related medical knowledge from coarse-level to fine-level hierarchically. Finally, a series of well-designed personalized templates are proposed to generate plausible and realistic answers in condition of the certain disease. We also conduct extensive user studies to demonstrate that the VSP satisfies the medical students' diagnosis practice requirement in terms of naturalness, realism, and topic relevance.
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Combining LLMs with knowledge graphs can create virtual standard patients for training and evaluation of medical students' diagnostic skills.
“Previous studies have shown the effectiveness of combining LLMs with knowledge graphs to create virtual standard patients, aiding the training and evaluation of medical students' diagnostic skills [61].”
From Promises and challenges of generative artificial intelligence for human learning (2024) · cites this paper as [61]
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“Only seven studies have taken a further step and conducted validation studies in authentic learning environments, with most functional components integrated into the educational tasks (TRL-5), such as an intelligent virtual standard patient for medical students training (Song et al., 2022) and an intelligent chatbot f…”
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Self-citation
Metadata
- Authors
- Song, W., Hou, X., Li, S., Chen, C., Gao, D., Wang, X., Sun, Y., Hou, J., & Hao, A.
- Affiliations
- Beijing Information Science & Technology University (CN)
- Peng Cheng Laboratory (CN)
- China University of Petroleum, East China (CN)
- Peking University (CN)
- Venue
- IEEE Transactions on Multimedia
- Published
- 2023
- Volume
- 25
- Pages
- 6132-6145
- ISSN
- 1520-9210 (ISSN-L)
- 1941-0077
- OpenAlex
- W4295886610
- Cited by
- 18
- References
- 60
- Citations by year
- 2026: 6 · 2025: 4 · 2024: 5 · 2023: 3
- FWCI
- 0.90
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Transactions on Multimedia (publishedVersion)
- Funders
- Beijing Natural Science Foundation (4222024)
- National Natural Science Foundation of China (62102036)
- R&D Program of Beijing Municipal Education Commission (KM202211232003)
- Open Project Program of State Key Laboratory of Virtual Reality Technology and Systems
- Beihang University (VRLAB2022A02)
- Research Unit of Virtual Human and Virtual Surgery
- Chinese Academy of Medical Sciences (2019RU004)
- Beijing Advanced Innovation Center for Biomedical Engineering (ZF138G1714)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Artificial Intelligence › Speech and dialogue systems
- Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
- Keywords
- oral diseases, symptom generation, chatbots
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