Application of DeepSeek-assisted problem-based learning in hematology residency training

作者：Jinxiao Hou, Furun An, Hui Qin, Lulu Zhang, Juan Wang, Cui Zhang, Dachuan Fan · 发表于：BMC Medical Education · 年份：2025 · DOI：10.1186/s12909-025-07852-x · 被引用次数：8 · 研究领域：Problem and Project Based Learning、Clinical Reasoning and Diagnostic Skills、Innovations in Medical Education

OBJECTIVES: This study aimed to evaluate the efficacy of integrating the open-source large language model (LLM) DeepSeek into problem-based learning (PBL) curriculum for hematology residency training. METHODS: This non-randomized controlled trial included two groups of 30 s-year hematology residents each. One group received traditional PBL instruction, while the other's PBL was assisted by DeepSeek. Both groups participated in in-person PBL sessions across two identical hematology courses. The DeepSeek-assisted PBL group utilized DeepSeek V3 and R1 models, along with an AI-facilitated web search and integrated output after automatic information filtering and analysis during their in-person PBL sessions. Learning outcomes were assessed via a post-course survey evaluating effectiveness, credibility, reliability, and engagement. Students also completed five standardized examinations covering analysis and diagnostic decision-making, procedural skills, communication, interdisciplinary integration, and emergency management/ethical considerations. RESULTS: The study demonstrated significant advantages of DeepSeek-assisted PBL over traditional PBL across multiple competency domains, including case analysis effectiveness, feedback quality, course structure, and clinical reasoning. Participants also reported stronger curriculum alignment with current guidelines and enhanced capacity for generating clinical insights during discussions. Academically, the DeepSeek-assisted PBL group outpe...