Exploring the ethical challenges of large language models in emergency medicine: A comparative international review
Elbattah, Mahmoud; Arnaud, Emilien; Ghazali, Daniel Aiham; Dequen, Gilles
Authors
Mahmoud Elbattah
Emilien Arnaud
Daniel Aiham Ghazali
Gilles Dequen
Abstract
Large Language Models (LLMs) hold promise for advancing Emergency Medicine by enhancing operational efficiency and supporting decision-making. This scoping review explores the ethical, legal, and global considerations influencing LLM deployment in emergency care. Key ethical concerns, including patient safety, data privacy, and transparency, emphasise the need for explainable AI (XAI) to build trust and prevent biased outputs. Legal challenges highlight the importance of regulatory compliance, especially regarding data protection laws like the GDPR. Significant international variability in LLM adoption further underscores the need for harmonised guidelines to ensure safe and equitable AI integration across diverse healthcare systems. To advance the responsible use of LLMs, future research should prioritise model transparency, consider resource-limited settings, and focus on establishing robust regulatory frameworks.
Presentation Conference Type
Conference Paper (published)
Conference Name
2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
Start Date
Dec 3, 2024
End Date
Dec 6, 2024
Acceptance Date
Nov 29, 2024
Online Publication Date
Jan 10, 2025
Publication Date
Jan 10, 2025
Deposit Date
Jan 13, 2025
Publicly Available Date
Jan 15, 2025
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Peer Reviewed
Peer Reviewed
Pages
5750-5755
Book Title
2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
