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Large Language Model Advances in Transfusion Medicine: From Answering Questions to Supporting Clinical Decisions

  • Sangzin Ahn (Department of Pharmacology and Pharmacogenomics Research Center, Inje University College of Medicine)
  • Published : 2025.09.01

Abstract

Keywords

References

  1. Thirunavukarasu AJ, Ting DSJ, Elangovan K, Gutierrez L, Tan TF, Ting DSW. Large language models in medicine. Nat Med 2023;29:1930–40. https://doi.org/10.1038/s41591-023-02448-8
  2. Singhal K, Azizi S, Tu T, Mahdavi SS, Wei J, Chung HW, et al. Large language models encode clinical knowledge. Nature 2023;620:172–80.
  3. You J, Seok HS, Kim S, Shin H. Advancing laboratory medicine practice with machine learning: swift yet exact. Ann Lab Med 2025;45:22–35. https://doi.org/10.3343/alm.2024.0354
  4. Maynard S, Farrington J, Alimam S, Evans H, Li K, Wong WK, et al. Machine learning in transfusion medicine: a scoping review. Transfusion 2024;64:162–84. https://doi.org/10.1111/trf.v64.1
  5. Lee JK, Choi S, Park S, Hwang SH, Cho D. Evaluation of six large language models for clinical decision support: application in transfusion decision-making for RhD blood-type patients. Ann Lab Med 2025;45: 520-29. https://doi.org/10.3343/alm.2024.0588
  6. Gargari OK, Habibi G. Enhancing medical AI with retrieval-augmented generation: a mini narrative review. Digit Health. 2025;11:20552076251337177. https://doi.org/10.1177/20552076251337177
  7. Ahn S. A guide to evade hallucinations and maintain reliability when using large language models for medical research: a narrative review. Ann Pediatr Endocrinol Metab 2025;30:115–18. https://doi.org/10.6065/apem.2448278.139
  8. Kaplinsky P, Singh R, Fusillo TF, Leader A, Zwicker JI, Mantha S. Retrieval augmented generation for the detection of major bleeding events in the electronic health record. Blood 2024;144(S1):2263. https://doi.org/10.1182/blood-2024-203911
  9. Wei J, Wang X, Schuurmans D, Bosma M, Xia F, Chi E, Le QV, Zhou D. Chain-of-thought prompting elicits reasoning in large language models. Adv Neural Inf Process Syst 2022;35:24824–37.
  10. Yang HS, Li J, Yi X, Wang F. Performance evaluation of large language models with chain-of-thought reasoning ability in clinical laboratory case interpretation. Clin Chem Lab Med 2025;63:e199–201. https://doi.org/10.1515/cclm-2025-0055
  11. Huang X, Wu J, Liu H, Tang X, Zhou Y. m1: Unleash the potential of test-time scaling for medical reasoning with large language models. arXiv [preprint] 2025;2504.00869. doi: 10.48550/arXiv.2504.00869.
  12. Wang X, Wei J, Schuurmans D, Le Q, Chi E, Narang S, Chowdhery A, Zhou D. Self-consistency improves chain of thought reasoning in language models. arXiv [preprint]. 2025;2203.11171. doi: 10.48550/arXiv.2203.11171.
  13. Yao S, Yu D, Zhao J, Shafran I, Griffiths T, Cao Y, Narasimhan K. Tree of thoughts: deliberate problem solving with large language models. Advances in neural information processing systems. 2023;36:11809–22.
  14. Singhal K, Tu T, Gottweis J, Sayres R, Wulczyn E, Amin M, Hou L, Clark K, Pfohl SR, Cole-Lewis H, Neal D. Toward expert-level medical question answering with large language models. Nat Med 2025;31:943–50. https://doi.org/10.1038/s41591-024-03423-7
  15. Sellergren A, Kazemzadeh S, Jaroensri T, Kiraly A, Traverse M, Kohlberger T, Xu S, Jamil F, Hughes C, Lau C, Chen J. MedGemma technical report. arXiv [preprint] 2025;2507.05201. doi: 10.48550/arXiv.2507.05201.
  16. Yuan H. Agentic large language models for healthcare: current progress and future opportunities. Med Adv 2025;3:37–41. https://doi.org/10.1002/med4.v3.1
  17. Yao S, Zhao J, Yu D, Du N, Shafran I, Narasimhan K, Cao Y. React: synergizing reasoning and acting in language models. International Conference on Learning Representations. 2023;10451467.
  18. Anthropic. How we built our multi-agent research system. https://www.anthropic.com/engineering/built-multi-agent-research-system (Updated on Jun 2025).