Participatory Assessment of Large Language Model Applications in an Academic Medical Center
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866910788981096448 |
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| author | Carra, Giorgia Kulynych, Bogdan Bastardot, François Kaufmann, Daniel E. Boillat-Blanco, Noémie Raisaro, Jean Louis |
| author_facet | Carra, Giorgia Kulynych, Bogdan Bastardot, François Kaufmann, Daniel E. Boillat-Blanco, Noémie Raisaro, Jean Louis |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_10366 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Participatory Assessment of Large Language Model Applications in an Academic Medical Center Carra, Giorgia Kulynych, Bogdan Bastardot, François Kaufmann, Daniel E. Boillat-Blanco, Noémie Raisaro, Jean Louis Computers and Society Artificial Intelligence Machine Learning 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. |
| title | Participatory Assessment of Large Language Model Applications in an Academic Medical Center |
| topic | Computers and Society Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2501.10366 |