ArticleBMC medical education2025
Application of DeepSeek-assisted problem-based learning in hematology residency training.
Article in BMC medical education, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it.
- AI-supported case-based learning in medical education: a comprehensive scoping review.Frontiers in medicine · 2026Pooled it
- Application of AI-assisted PBL-CBL combined teaching in the diagnosis and treatment of post-herpetic neuralgia: a randomised controlled trial.BMC medical education · 2026Trial
- Hematology-Oncology Fellows' Use of Artificial Intelligence: A Multicenter Educational Practice and Needs Assessment Survey.Journal of cancer education : the official journal of the American Association for Cancer Education · 2026Article
- Effectiveness of Problem-Based Learning (PBL) in specialist physician training for kidney transplantation.Medicine · 2026Article
- Effects of Artificial Intelligence-Supported Education on Critical Thinking, Problem-Solving, Clinical Reasoning, and Decision-Making in Health Science Education: A Systematic Review of Interventional Studies.Advances in medical education and practice · 2026Review
- The "DeepSeek effect" and the adoption-integration gap of generative artificial intelligence in clinical practice: a national online convenience cross-sectional survey of academic critical care physicians in China.Frontiers in medicine · 2026Article
- Hotspot Evolution and Future Prospects of Large Language Models in Medical Education: A Bibliometric Analysis.Advances in medical education and practice · 2026Article
- Psychological mechanisms underlying medical students' continued use of DeepSeek: an explanatory sequential mixed-methods study.Frontiers in psychology · 2026Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
objectivesThis 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.
methodsThis 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.
resultsThe 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.
conclusionIntegrating 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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.