Transforming hematological research documentation with large language models:an approach to scientific writing and data analysis
10.1007/s44313-025-00062-w
- Author:
John Jeongseok YANG
1
;
Sang‑Hyun HWANG
Author Information
1. Department of Laboratory Medicine and Genetics, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 06351, Republic of Korea
- Publication Type:REVIEW
- From:Blood Research
2025;60():15-
- CountryRepublic of Korea
- Language:English
-
Abstract:
Large Language Models (LLMs), such as ChatGPT (OpenAI, CA, US), have revolutionized scientific writing and research processes across academic disciplines, providing comprehensive support throughout the entire research lifecycle.Generative artificial intelligence (GAI) tools enhance every aspect of scientific writing, from hypothesis genera‑ tion and methodology design to data analysis and manuscript preparation. This review examines the applications of LLMs in hematological research, with particular emphasis on advanced techniques, including prompt engineering and retrieval augmented generation (RAG) frameworks. Prompt engineering methods, including zero-shot and fewshot learning along with a chain-of-thought approach, enable researchers to generate more precise context-specific content, especially in scientific writing. Integrating RAG frameworks with the current medical literature and clinical guidelines significantly reduces the risk of misinformation while ensuring alignment with contemporary medical standards. Even though these GAI tools offer remarkable potential for streamlining research writing and enhancing documentation quality, the study also addresses the critical importance of maintaining scientific integrity, ethical considerations, and privacy concerns in hematological research.
