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Exploring the Ethical Challenges of Large Language Models in Emergency Medicine: A Comparative International Review

Year: 2024

Type: conference-paper

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.

Source: IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

DOI: 10.1109/bibm62325.2024.10822376

Authors: Mahmoud Elbattah, Emilien Arnaud, Daniel Aiham Ghazali, Gilles Dequen

Institutions: Modélisation, information et systèmes, Université de Picardie Jules Verne, Centre Hospitalier Universitaire Amiens-Picardie

Language: English

Topic: Artificial Intelligence in Healthcare and Education

Subfield: Health Informatics

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: green

OpenAlex ID: W4406261434