Exploring the role of Large Language Models in haematology: A focused review of applications, benefits and limitations.

Autor: Mudrik A; Ben-Gurion University of the Negev, Be'er Sheva, Israel., Nadkarni GN; The Division of Data-Driven and Digital Medicine (D3M), Icahn School of Medicine at Mount Sinai, New York, New York, USA.; The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA., Efros O; Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel.; National Hemophilia Center and Institute of Thrombosis & Hemostasis, Chaim Sheba Medical Center, Tel Hashomer, Israel., Glicksberg BS; The Division of Data-Driven and Digital Medicine (D3M), Icahn School of Medicine at Mount Sinai, New York, New York, USA.; The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA., Klang E; The Division of Data-Driven and Digital Medicine (D3M), Icahn School of Medicine at Mount Sinai, New York, New York, USA.; The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA., Soffer S; Institute of Hematology, Davidoff Cancer Center, Rabin Medical Center, Petah-Tikva, Israel.
Jazyk: angličtina
Zdroj: British journal of haematology [Br J Haematol] 2024 Nov; Vol. 205 (5), pp. 1685-1698. Date of Electronic Publication: 2024 Sep 03.
DOI: 10.1111/bjh.19738
Abstrakt: Large language models (LLMs) have significantly impacted various fields with their ability to understand and generate human-like text. This study explores the potential benefits and limitations of integrating LLMs, such as ChatGPT, into haematology practices. Utilizing systematic review methodologies, we analysed studies published after 1 December 2022, from databases like PubMed, Web of Science and Scopus, and assessing each for bias with the QUADAS-2 tool. We reviewed 10 studies that applied LLMs in various haematology contexts. These models demonstrated proficiency in specific tasks, such as achieving 76% diagnostic accuracy for haemoglobinopathies. However, the research highlighted inconsistencies in performance and reference accuracy, indicating variability in reliability across different uses. Additionally, the limited scope of these studies and constraints on datasets could potentially limit the generalizability of our findings. The findings suggest that, while LLMs provide notable advantages in enhancing diagnostic processes and educational resources within haematology, their integration into clinical practice requires careful consideration. Before implementing them in haematology, rigorous testing and specific adaptation are essential. This involves validating their accuracy and reliability across different scenarios. Given the field's complexity, it is also critical to continuously monitor these models and adapt them responsively.
(© 2024 The Author(s). British Journal of Haematology published by British Society for Haematology and John Wiley & Sons Ltd.)
Databáze: MEDLINE
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