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

Abstract

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 outperformed in four out of five competency domains (Exams I, III, IV, V), achieving higher total examination scores. However, no significant difference emerged in clinical skills (Exam II), nor did DeepSeek enhance interactive elements based on survey results. Notably, the DeepSeek-assisted PBL group also expressed greater concerns about the potential inaccuracies in artificial intelligence-generated medical advice.

Conclusion: Integrating DeepSeek into the PBL curriculum may improve clinical competence, diagnostic reasoning, and learner engagement in hematology residency training. These findings suggest that open-source LLMs like DeepSeek may offer scalable and cost-effective support tools to augment traditional medical education. Further study is needed to explore artificial intelligence tools for enhancing interactive elements and procedural skills.

Keywords: DeepSeek; Hematology residency; Large language model; Medical education; Problem-based learning.