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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2023
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2312.11803 |
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| _version_ | 1866909081023807488 |
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| author | Antoniak, Maria Naik, Aakanksha Alvarado, Carla S. Wang, Lucy Lu Chen, Irene Y. |
| author_facet | Antoniak, Maria Naik, Aakanksha Alvarado, Carla S. Wang, Lucy Lu Chen, Irene Y. |
| contents | Ethical frameworks for the use of natural language processing (NLP) are urgently needed to shape how large language models (LLMs) and similar tools are used for healthcare applications. Healthcare faces existing challenges including the balance of power in clinician-patient relationships, systemic health disparities, historical injustices, and economic constraints. Drawing directly from the voices of those most affected, and focusing on a case study of a specific healthcare setting, we propose a set of guiding principles for the use of NLP in maternal healthcare. We led an interactive session centered on an LLM-based chatbot demonstration during a full-day workshop with 39 participants, and additionally surveyed 30 healthcare workers and 30 birthing people about their values, needs, and perceptions of NLP tools in the context of maternal health. We conducted quantitative and qualitative analyses of the survey results and interactive discussions to consolidate our findings into a set of guiding principles. We propose nine principles for ethical use of NLP for maternal healthcare, grouped into three themes: (i) recognizing contextual significance (ii) holistic measurements, and (iii) who/what is valued. For each principle, we describe its underlying rationale and provide practical advice. This set of principles can provide a methodological pattern for other researchers and serve as a resource to practitioners working on maternal health and other healthcare fields to emphasize the importance of technical nuance, historical context, and inclusive design when developing NLP technologies for clinical use. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2312_11803 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | NLP for Maternal Healthcare: Perspectives and Guiding Principles in the Age of LLMs Antoniak, Maria Naik, Aakanksha Alvarado, Carla S. Wang, Lucy Lu Chen, Irene Y. Computation and Language Ethical frameworks for the use of natural language processing (NLP) are urgently needed to shape how large language models (LLMs) and similar tools are used for healthcare applications. Healthcare faces existing challenges including the balance of power in clinician-patient relationships, systemic health disparities, historical injustices, and economic constraints. Drawing directly from the voices of those most affected, and focusing on a case study of a specific healthcare setting, we propose a set of guiding principles for the use of NLP in maternal healthcare. We led an interactive session centered on an LLM-based chatbot demonstration during a full-day workshop with 39 participants, and additionally surveyed 30 healthcare workers and 30 birthing people about their values, needs, and perceptions of NLP tools in the context of maternal health. We conducted quantitative and qualitative analyses of the survey results and interactive discussions to consolidate our findings into a set of guiding principles. We propose nine principles for ethical use of NLP for maternal healthcare, grouped into three themes: (i) recognizing contextual significance (ii) holistic measurements, and (iii) who/what is valued. For each principle, we describe its underlying rationale and provide practical advice. This set of principles can provide a methodological pattern for other researchers and serve as a resource to practitioners working on maternal health and other healthcare fields to emphasize the importance of technical nuance, historical context, and inclusive design when developing NLP technologies for clinical use. |
| title | NLP for Maternal Healthcare: Perspectives and Guiding Principles in the Age of LLMs |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2312.11803 |