# Using large language models to promote health equity

arXiv: https://arxiv.org/abs/2312.14804 · HTML: https://mlsanity.com/paper/2312.14804/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Dec 2023.

## Built on by (most cited first, 6 of 6)

- [Participation in the age of foundation models](https://mlsanity.com/paper/2405.19479/) (2024)
- [Sparse Autoencoders for Hypothesis Generation](https://mlsanity.com/paper/2502.04382/) (2025)
- [JudgmentBench: Comparing Rubric and Preference Evaluation for Quality Assessment](https://mlsanity.com/paper/2605.25240/) (2026)
- [Reflections from Research Roundtables at the Conference on Health, Inference, and Learning (CHIL) 2025](https://mlsanity.com/paper/2510.15217/) (2025)
- [The Application of MATEC (Multi-AI Agent Team Care) Framework in Sepsis Care](https://mlsanity.com/paper/2503.16433/) (2025)
- [Fine-Tune, Don't Prompt, Your Language Model to Identify Biased Language in Clinical Notes](https://mlsanity.com/paper/2603.10004/) (2026)

## Abstract (arXiv)

Advances in large language models (LLMs) have driven an explosion of interest about their societal impacts. Much of the discourse around how they will impact social equity has been cautionary or negative, focusing on questions like "how might LLMs be biased and how would we mitigate those biases?" This is a vital discussion: the ways in which AI generally, and LLMs specifically, can entrench biases have been well-documented. But equally vital, and much less discussed, is the more opportunity-focused counterpoint: "what promising applications do LLMs enable that could promote equity?" If LLMs are to enable a more equitable world, it is not enough just to play defense against their biases and failure modes. We must also go on offense, applying them positively to equity-enhancing use cases to increase opportunities for underserved groups and reduce societal discrimination. There are many choices which determine the impact of AI, and a fundamental choice very early in the pipeline is the problems we choose to apply it to. If we focus only later in the pipeline -- making LLMs marginally more fair as they facilitate use cases which intrinsically entrench power -- we will miss an important opportunity to guide them to equitable impacts. Here, we highlight the emerging potential of LLMs to promote equity by presenting four newly possible, promising research directions, while keeping risks and cautionary points in clear view.