Bridging the gap: a practical step-by-step approach to warrant safe implementation of large language models in healthcare
Large Language Models (LLMs) offer considerable potential to enhance various aspects of healthcare, from aiding with administrative tasks to clinical decision support. However, despite the growing use of LLMs in healthcare, a critical gap persists in clear, actionable guidelines available to healthc...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2025-01-01
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| Series: | Frontiers in Artificial Intelligence |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/frai.2025.1504805/full |
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| author | Jessica D. Workum Jessica D. Workum Jessica D. Workum Davy van de Sande Davy van de Sande Diederik Gommers Diederik Gommers Michel E. van Genderen Michel E. van Genderen |
| author_facet | Jessica D. Workum Jessica D. Workum Jessica D. Workum Davy van de Sande Davy van de Sande Diederik Gommers Diederik Gommers Michel E. van Genderen Michel E. van Genderen |
| author_sort | Jessica D. Workum |
| collection | DOAJ |
| description | Large Language Models (LLMs) offer considerable potential to enhance various aspects of healthcare, from aiding with administrative tasks to clinical decision support. However, despite the growing use of LLMs in healthcare, a critical gap persists in clear, actionable guidelines available to healthcare organizations and providers to ensure their responsible and safe implementation. In this paper, we propose a practical step-by-step approach to bridge this gap and support healthcare organizations and providers in warranting the responsible and safe implementation of LLMs into healthcare. The recommendations in this manuscript include protecting patient privacy, adapting models to healthcare-specific needs, adjusting hyperparameters appropriately, ensuring proper medical prompt engineering, distinguishing between clinical decision support (CDS) and non-CDS applications, systematically evaluating LLM outputs using a structured approach, and implementing a solid model governance structure. We furthermore propose the ACUTE mnemonic; a structured approach for assessing LLM responses based on Accuracy, Consistency, semantically Unaltered outputs, Traceability, and Ethical considerations. Together, these recommendations aim to provide healthcare organizations and providers with a clear pathway for the responsible and safe implementation of LLMs into clinical practice. |
| format | Article |
| id | doaj-art-4a5ea5eec6fe483e8cb9532f72ee2f9b |
| institution | Kabale University |
| issn | 2624-8212 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Artificial Intelligence |
| spelling | doaj-art-4a5ea5eec6fe483e8cb9532f72ee2f9b2025-01-27T12:30:58ZengFrontiers Media S.A.Frontiers in Artificial Intelligence2624-82122025-01-01810.3389/frai.2025.15048051504805Bridging the gap: a practical step-by-step approach to warrant safe implementation of large language models in healthcareJessica D. Workum0Jessica D. Workum1Jessica D. Workum2Davy van de Sande3Davy van de Sande4Diederik Gommers5Diederik Gommers6Michel E. van Genderen7Michel E. van Genderen8Department of Adult Intensive Care, Erasmus MC University Medical Center, Rotterdam, NetherlandsDepartment of Intensive Care, Elisabeth-TweeSteden Hospital, Tilburg, NetherlandsErasmus MC Datahub, Erasmus MC University Medical Center, Rotterdam, NetherlandsDepartment of Adult Intensive Care, Erasmus MC University Medical Center, Rotterdam, NetherlandsErasmus MC Datahub, Erasmus MC University Medical Center, Rotterdam, NetherlandsDepartment of Adult Intensive Care, Erasmus MC University Medical Center, Rotterdam, NetherlandsErasmus MC Datahub, Erasmus MC University Medical Center, Rotterdam, NetherlandsDepartment of Adult Intensive Care, Erasmus MC University Medical Center, Rotterdam, NetherlandsErasmus MC Datahub, Erasmus MC University Medical Center, Rotterdam, NetherlandsLarge Language Models (LLMs) offer considerable potential to enhance various aspects of healthcare, from aiding with administrative tasks to clinical decision support. However, despite the growing use of LLMs in healthcare, a critical gap persists in clear, actionable guidelines available to healthcare organizations and providers to ensure their responsible and safe implementation. In this paper, we propose a practical step-by-step approach to bridge this gap and support healthcare organizations and providers in warranting the responsible and safe implementation of LLMs into healthcare. The recommendations in this manuscript include protecting patient privacy, adapting models to healthcare-specific needs, adjusting hyperparameters appropriately, ensuring proper medical prompt engineering, distinguishing between clinical decision support (CDS) and non-CDS applications, systematically evaluating LLM outputs using a structured approach, and implementing a solid model governance structure. We furthermore propose the ACUTE mnemonic; a structured approach for assessing LLM responses based on Accuracy, Consistency, semantically Unaltered outputs, Traceability, and Ethical considerations. Together, these recommendations aim to provide healthcare organizations and providers with a clear pathway for the responsible and safe implementation of LLMs into clinical practice.https://www.frontiersin.org/articles/10.3389/frai.2025.1504805/fulllarge language modelsresponsible AIartificial intelligencehealth care qualityaccess and evaluationdisruptive technology |
| spellingShingle | Jessica D. Workum Jessica D. Workum Jessica D. Workum Davy van de Sande Davy van de Sande Diederik Gommers Diederik Gommers Michel E. van Genderen Michel E. van Genderen Bridging the gap: a practical step-by-step approach to warrant safe implementation of large language models in healthcare Frontiers in Artificial Intelligence large language models responsible AI artificial intelligence health care quality access and evaluation disruptive technology |
| title | Bridging the gap: a practical step-by-step approach to warrant safe implementation of large language models in healthcare |
| title_full | Bridging the gap: a practical step-by-step approach to warrant safe implementation of large language models in healthcare |
| title_fullStr | Bridging the gap: a practical step-by-step approach to warrant safe implementation of large language models in healthcare |
| title_full_unstemmed | Bridging the gap: a practical step-by-step approach to warrant safe implementation of large language models in healthcare |
| title_short | Bridging the gap: a practical step-by-step approach to warrant safe implementation of large language models in healthcare |
| title_sort | bridging the gap a practical step by step approach to warrant safe implementation of large language models in healthcare |
| topic | large language models responsible AI artificial intelligence health care quality access and evaluation disruptive technology |
| url | https://www.frontiersin.org/articles/10.3389/frai.2025.1504805/full |
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