Large language models for chart review: how machine learning can accelerate hematology research

Barbara D Lam^{1

2}, Peiqi Wang³, Shengling Ma⁴, Omid Jafari⁵, Iuliia Kovalenko⁶, Ang Li⁴

Affiliations

¹ Division of Hematology, Department of Medicine, Beth Israel Deaconess Medical Center, Boston, MA.
² Division of Clinical Informatics, Department of Medicine, Beth Israel Deaconess Medical Center, Boston, MA.
³ Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA.
⁴ Section of Hematology-Oncology, Department of Medicine, Baylor College of Medicine, Houston, TX.
⁵ Institute for Clinical and Translational Research, Baylor College of Medicine, Houston, TX.
⁶ Department of Medicine, University of Pittsburgh Medical Center, Harrisburg, PA.

PMID: 40766872
PMCID: PMC12320455
DOI: 10.1016/j.bvth.2025.100052

Large language models for chart review: how machine learning can accelerate hematology research

Barbara D Lam et al. Blood Vessel Thromb Hemost. 2025.

. 2025 Jan 15;2(1):100052.

doi: 10.1016/j.bvth.2025.100052. eCollection 2025 Feb.

Authors

Barbara D Lam^{1

2}, Peiqi Wang³, Shengling Ma⁴, Omid Jafari⁵, Iuliia Kovalenko⁶, Ang Li⁴

Affiliations

¹ Division of Hematology, Department of Medicine, Beth Israel Deaconess Medical Center, Boston, MA.
² Division of Clinical Informatics, Department of Medicine, Beth Israel Deaconess Medical Center, Boston, MA.
³ Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA.
⁴ Section of Hematology-Oncology, Department of Medicine, Baylor College of Medicine, Houston, TX.
⁵ Institute for Clinical and Translational Research, Baylor College of Medicine, Houston, TX.
⁶ Department of Medicine, University of Pittsburgh Medical Center, Harrisburg, PA.

PMID: 40766872
PMCID: PMC12320455
DOI: 10.1016/j.bvth.2025.100052

No abstract available

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Conflict of interest statement

Conflict-of-interest disclosure: P.W. completed a summer internship with Meta. The conception and completion of this study took place before knowledge of the internship. The remaining authors declare no competing financial interests. The current affiliation for B.D.L. is Division of Hematology and Oncology, Department of Medicine, Fred Hutchinson Cancer Center, University of Washington Medical Center, Seattle, WA.

References

1. Lam BD, Chrysafi P, Chiasakul T, et al. Machine learning natural language processing for identifying venous thromboembolism: systematic review and meta-analysis. Blood Adv. 2024;8(12):2991–3000. - PMC - PubMed
1. Thirunavukarasu AJ, Ting DSJ, Elangovan K, Gutierrez L, Tan TF, Ting DSW. Large language models in medicine. Nat Med. 2023;29(8):1930–1940. - PubMed
1. Yang X, Chen A, PourNejatian N, et al. A large language model for electronic health records. NPJ Digit Med. 2022;5(1):194. - PMC - PubMed
1. Lee J, Yoon W, Kim S, et al. BioBERT: a pre-trained biomedical language representation model for biomedical text mining. Bioinformatics. 2020;36(4):1234–1240. - PMC - PubMed
1. Ge J, Li M, Delk MB, Lai JC. A comparison of a large language model vs manual chart review for the extraction of data elements from the electronic health record. Gastroenterology. 2024;166(4):707–709.e3. - PMC - PubMed

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Large language models for chart review: how machine learning can accelerate hematology research

Affiliations

Large language models for chart review: how machine learning can accelerate hematology research

Authors

Affiliations

Conflict of interest statement

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