Content area
Background
Digital 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.
Methods
Following 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.
Results
The 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.
Conclusion
The 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.
Details
Atlases;
Data Collection;
Interviews;
Knowledge Representation;
Medical Evaluation;
Medical Students;
Instructional Effectiveness;
Course Content;
Learner Engagement;
Constructivism (Learning);
Methods Research;
Educational Strategies;
Algorithms;
Educational Resources;
Control Groups;
Influence of Technology;
Medical Education;
Learning Theories;
Educational Technology;
Efficiency;
Etiology;
Intelligence;
Flipped Classroom;
Data Analysis
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