Research on the construction and application of pathology knowledge graph
Abstract 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 co...
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| Format: | Article |
| Language: | English |
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BMC
2025-07-01
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| Series: | BMC Medical Education |
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| Online Access: | https://doi.org/10.1186/s12909-025-07566-0 |
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| author | Hong Wei Xue Liu Huiling Cao Weiqi Qin Qun Ma Lingling Kong |
| author_facet | Hong Wei Xue Liu Huiling Cao Weiqi Qin Qun Ma Lingling Kong |
| author_sort | Hong Wei |
| collection | DOAJ |
| description | Abstract 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. |
| format | Article |
| id | doaj-art-47397492e6fd488e93a151ec97faf3b5 |
| institution | Kabale University |
| issn | 1472-6920 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Medical Education |
| spelling | doaj-art-47397492e6fd488e93a151ec97faf3b52025-08-20T03:38:18ZengBMCBMC Medical Education1472-69202025-07-0125111110.1186/s12909-025-07566-0Research on the construction and application of pathology knowledge graphHong Wei0Xue Liu1Huiling Cao2Weiqi Qin3Qun Ma4Lingling Kong5College of Basic Medicine, Jining Medical UniversityCollege of Basic Medicine, Jining Medical UniversityCollege of Basic Medicine, Jining Medical UniversityCollege of Clinical Medicine, Jining Medical UniversityCenter for Excellent Teaching and Learning, Jining Medical UniversityCollege of Basic Medicine, Jining Medical UniversityAbstract 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.https://doi.org/10.1186/s12909-025-07566-0Knowledge graphPathologyFlipped classroomAdaptive learning |
| spellingShingle | Hong Wei Xue Liu Huiling Cao Weiqi Qin Qun Ma Lingling Kong Research on the construction and application of pathology knowledge graph BMC Medical Education Knowledge graph Pathology Flipped classroom Adaptive learning |
| title | Research on the construction and application of pathology knowledge graph |
| title_full | Research on the construction and application of pathology knowledge graph |
| title_fullStr | Research on the construction and application of pathology knowledge graph |
| title_full_unstemmed | Research on the construction and application of pathology knowledge graph |
| title_short | Research on the construction and application of pathology knowledge graph |
| title_sort | research on the construction and application of pathology knowledge graph |
| topic | Knowledge graph Pathology Flipped classroom Adaptive learning |
| url | https://doi.org/10.1186/s12909-025-07566-0 |
| work_keys_str_mv | AT hongwei researchontheconstructionandapplicationofpathologyknowledgegraph AT xueliu researchontheconstructionandapplicationofpathologyknowledgegraph AT huilingcao researchontheconstructionandapplicationofpathologyknowledgegraph AT weiqiqin researchontheconstructionandapplicationofpathologyknowledgegraph AT qunma researchontheconstructionandapplicationofpathologyknowledgegraph AT linglingkong researchontheconstructionandapplicationofpathologyknowledgegraph |