ArticleCureus2026
A Medical Large Language Model-Based System Improves History-Taking Performance Among Medical Students.
Article in Cureus, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Authors and funding
4 authors.
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Abstract
introductionTo develop a history-taking training and assessment system based on a medical large language model (MedLLM), evaluate its effect on medical students' history-taking competence, and examine its feasibility and effectiveness as a supplement to conventional interview teaching.
methodsAn intelligent history-taking training and assessment system was built around a domain-fine-tuned Qwen2.5 large language model. Fifty medical students were assigned in equal numbers to an intervention group and a control group (25 each) using a random number table. The intervention group trained with the system, whereas the control group continued to practice through traditional role-play. Outcomes included system acceptance, practice satisfaction, history-taking examination scores, and agreement between manual and automated scoring.
resultsThe intervention group rated the system most favorably for feasibility (4.21±0.95) and interest level (3.76±1.14) (P<0.05), and reported significantly greater satisfaction with flexibility (4.06±0.74) and efficiency (4.10±0.60) than the control group (P<0.001). In the manual examination, the intervention group outperformed the control group (84.31±3.92 vs. 80.56±3.41; P<0.05). Overall agreement between automated and manual scoring was good (intraclass correlation coefficient (ICC)=0.76). It was highest for logical organization (ICC=0.87) and clinical reasoning (ICC=0.85) and moderate for communication skills (ICC=0.63) and humanistic care (ICC=0.65).
conclusionAs a useful complement to traditional teaching, the MedLLM-based history-taking training and assessment system provides a flexible and efficient practice platform that meaningfully strengthens students' history-taking ability. Its automated scoring agrees well with manual assessment, indicating strong potential for clinical skills education.
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