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Research on the construction and application of pathology knowledge graph

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Argitaratua izan da:BMC Medical Education vol. 25 (2025), p. 1-12
Egile nagusia: Hong, Wei
Beste egile batzuk: Liu, Xue, Cao, Huiling, Qin, Weiqi, Ma, Qun, Kong, Lingling
Argitaratua:
Springer Nature B.V.
Gaiak:
Teaching
Medical education
Coding theory
Questionnaires
Educational technology
Cognition & reasoning
Efficiency
Etiology
Digital transformation
Flipped classroom
Knowledge
Quasi-experimental methods
Learning
Semantics
Design of experiments
Clinical medicine
Ontology
Medical students
Pathology
Interviews
Use statistics
Data collection
Morphology
Instructional scaffolding
Instructional Improvement
Atlases
Knowledge Representation
Medical Evaluation
Instructional Effectiveness
Course Content
Learner Engagement
Constructivism (Learning)
Methods Research
Educational Strategies
Algorithms
Educational Resources
Control Groups
Influence of Technology
Learning Theories
Intelligence
Data Analysis
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Laburpena:BackgroundDigital transformation in pathology education faces three bottlenecks: fragmented knowledge transfer, low morphological diagnostic accuracy, and weak clinical reasoning. While knowledge graphs (KGs) offer potential solutions, existing medical KG lack multimodal integration and competency assessment. We designed an integrated Multimodal Knowledge Graph (MKG) with O-PIRTAS pedagogy to bridge these gaps.MethodsFollowing Design Science Research Methodology, we built a pathology-specific MKG featuring: (1) Semantic modeling of disease mechanisms (etiology-pathogenesis-morphology-clinical), (2) Cross-modal alignment of digital slides/animations/clinical cases, (3) Embedded metrics (KII/MDA/CCAE) for competency quantification. A quasi-experiment with 533 medical students (2022 cohort control: n = 275; 2023 MKG-O-PIRTAS: n = 258) evaluated outcomes via exam scores, validated questionnaires, and stratified interviews.ResultsThe MKG-O-PIRTAS group achieved significantly higher adjusted exam scores (76.14 vs. 73.72, p = 0.033) and 86% lower misdiagnosis rate in high performers (p = 0.015). Cognitive load diverged markedly (57.5 vs. 75.5, p = 0.007), with high performers dynamically contextualizing MKG nodes into clinical reasoning, while novices required scaffolded pathways. Over 80% of students endorsed enhanced knowledge integration and process optimization.ConclusionThe MKG-O-PIRTAS artifact transforms scattered pathology knowledge into actionable clinical reasoning scaffolds, proving effective for personalized competency development. Future work will scale adaptive scaffolding and integrate real-time EMR modules, establishing a replicable paradigm for medical education intelligence.
ISSN:1472-6920
DOI:10.1186/s12909-025-07566-0
Baliabidea:Healthcare Administration Database