# NLP for Maternal Healthcare: Perspectives and Guiding Principles in the Age of LLMs arXiv: https://arxiv.org/abs/2312.11803 ยท HTML: https://mlsanity.com/paper/2312.11803/ ## Key facts - Cited by 14 papers in the map (data through 14 Oct 2026). - Builds on 25 of its 125 references that are in the map. - First posted on arXiv 19 Dec 2023. ## Builds on (most cited first, 25 of 25) - [Ethical and social risks of harm from Language Models](https://mlsanity.com/paper/2112.04359/) (2021) - [Semantics derived automatically from language corpora contain human-like biases](https://mlsanity.com/paper/1608.07187/) (2016) - [Capabilities of GPT-4 on Medical Challenge Problems](https://mlsanity.com/paper/2303.13375/) (2023) - [Men Also Like Shopping: Reducing Gender Bias Amplification using Corpus-level Constraints](https://mlsanity.com/paper/1707.09457/) (2017) - [AI and the Everything in the Whole Wide World Benchmark](https://mlsanity.com/paper/2111.15366/) (2021) - [Ethical Machine Learning in Health Care](https://mlsanity.com/paper/2009.10576/) (2020) - [Participation is not a Design Fix for Machine Learning](https://mlsanity.com/paper/2007.02423/) (2020) - [The Participatory Turn in AI Design: Theoretical Foundations and the Current State of Practice](https://mlsanity.com/paper/2310.00907/) (2023) - [A Systematic Study of Bias Amplification](https://mlsanity.com/paper/2201.11706/) (2022) - [The Use and Misuse of Counterfactuals in Ethical Machine Learning](https://mlsanity.com/paper/2102.05085/) (2021) - [How Different Groups Prioritize Ethical Values for Responsible AI](https://mlsanity.com/paper/2205.07722/) (2022) - [Enhancing Clinical Concept Extraction with Contextual Embeddings](https://mlsanity.com/paper/1902.08691/) (2019) - [The Shifted and The Overlooked: A Task-oriented Investigation of User-GPT Interactions](https://mlsanity.com/paper/2310.12418/) (2023) - [Clinical Outcome Prediction from Admission Notes using Self-Supervised Knowledge Integration](https://mlsanity.com/paper/2102.04110/) (2021) - [The Road to Explainability is Paved with Bias: Measuring the Fairness of Explanations](https://mlsanity.com/paper/2205.03295/) (2022) - ["The Human Body is a Black Box": Supporting Clinical Decision-Making with Deep Learning](https://mlsanity.com/paper/1911.08089/) (2019) - [Ignore, Trust, or Negotiate: Understanding Clinician Acceptance of AI-Based Treatment Recommendations in Health Care](https://mlsanity.com/paper/2302.00096/) (2023) - [Who Goes First? Influences of Human-AI Workflow on Decision Making in Clinical Imaging](https://mlsanity.com/paper/2205.09696/) (2022) - [Healthsheet: Development of a Transparency Artifact for Health Datasets](https://mlsanity.com/paper/2202.13028/) (2022) - [Designing Participatory AI: Creative Professionals' Worries and Expectations about Generative AI](https://mlsanity.com/paper/2303.08931/) (2023) - [Improving Fairness in AI Models on Electronic Health Records: The Case for Federated Learning Methods](https://mlsanity.com/paper/2305.11386/) (2023) - [A Mixed-Methods Approach to Understanding User Trust after Voice Assistant Failures](https://mlsanity.com/paper/2303.00164/) (2023) - [In the Name of Fairness: Assessing the Bias in Clinical Record De-identification](https://mlsanity.com/paper/2305.11348/) (2023) - [TalkUp: Paving the Way for Understanding Empowering Language](https://mlsanity.com/paper/2305.14326/) (2023) - [Organizational Governance of Emerging Technologies: AI Adoption in Healthcare](https://mlsanity.com/paper/2304.13081/) (2023) ## Built on by (most cited first, 14 of 14) - [Trust No Bot: Discovering Personal Disclosures in Human-LLM Conversations in the Wild](https://mlsanity.com/paper/2407.11438/) (2024) - [A Decision-Language Model (DLM) for Dynamic Restless Multi-Armed Bandit Tasks in Public Health](https://mlsanity.com/paper/2402.14807/) (2024) - [A Collection of Innovations in Medical AI for patient records in 2024](https://mlsanity.com/paper/2503.05768/) (2025) - [XBreaking: Understanding how LLMs security alignment can be broken](https://mlsanity.com/paper/2504.21700/) (2025) - [Revealing Treatment Non-Adherence Bias in Clinical Machine Learning Using Large Language Models](https://mlsanity.com/paper/2502.19625/) (2025) - [Who's Asking? Simulating Role-Based Questions for Conversational AI Evaluation](https://mlsanity.com/paper/2510.16829/) (2025) - [Developing and evaluating a chatbot to support maternal health care](https://mlsanity.com/paper/2603.13168/) (2026) - [A Risk Taxonomy and Reflection Tool for Large Language Model Adoption in Public Health](https://mlsanity.com/paper/2411.02594/) (2024) - [Generative AI in Medicine](https://mlsanity.com/paper/2412.10337/) (2024) - [Designing Beyond Language: Sociotechnical Barriers in AI Health Technologies for Limited English Proficiency](https://mlsanity.com/paper/2511.07277/) (2025) - [Human-Centric NLP or AI-Centric Illusion?: A Critical Investigation](https://mlsanity.com/paper/2412.10939/) (2024) - [Performance Gains of LLMs With Humans in a World of LLMs Versus Humans](https://mlsanity.com/paper/2505.08902/) (2025) - [Fragments to Facts: Partial-Information Fragment Inference from LLMs](https://mlsanity.com/paper/2505.13819/) (2025) - [Empathy Applicability Modeling for General Health Queries](https://mlsanity.com/paper/2601.09696/) (2026) ## Abstract (arXiv) 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.