Skip to yearly menu bar Skip to main content


Poster
in
Workshop: Regulatable ML: Towards Bridging the Gaps between Machine Learning Research and Regulations

Position: Participatory Assessment of Large Language Model Applications in an Academic Medical Center

Giorgia Carra · Bogdan Kulynych · François Bastardot · Noémie Boillat-Blanco · Jean Raisaro


Abstract:

Although Large Language Models (LLMs) have shown promising performance in healthcare-related applications, their deployment in the medical domain poses unique challenges of ethical, regulatory, and technical nature. In this study, we employ a systematic participatory approach to investigate the needs and expectations regarding clinical applications of LLMs at Lausanne University Hospital, an academic medical center in Switzerland. Having identified potential LLM use-cases in collaboration with thirty stakeholders, including clinical staff across 11 departments as well nursing and patient representatives, we assess the current feasibility of these use-cases taking into account the regulatory frameworks, data protection regulation, bias, hallucinations, and deployment constraints. This study provides a framework for a participatory approach to identifying institutional needs with respect to introducing advanced technologies into healthcare practice, and a realistic analysis of the technology readiness level of LLMs for medical applications, highlighting the issues that would need to be overcome LLMs in healthcare to be ethical, and regulatory compliant.

Chat is not available.