Using large language models to promote health equity

2023-12-22

AI Summary

Current AI applications in healthcare prioritize risk mitigation over positive social impact, neglecting opportunities to advance health equity. Method: This study proposes four emerging, “offensive” application paradigms for large language models (LLMs) that intervene proactively—from problem definition onward—rather than relying solely on retrospective correction. These include multimodal health information generation, cross-lingual health Q&A, personalized health education adaptation, and clinical communication optimization. We develop the first feasibility framework for LLM-enabled health equity, identifying high-potential use cases and co-designing integrated risk-mitigation mechanisms. Contribution/Results: The framework provides theoretical grounding and actionable pathways for steering AI toward inclusivity and justice. It advances equitable healthcare access, population-level health literacy, and fair clinical decision-making—particularly for marginalized and underserved populations—thereby operationalizing ethical AI deployment in global health contexts.

Abstract

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.

Problem

• Large Language Models
• Social Equity
• Healthcare Applications

Innovation

• Large Language Models
• Social Equity
• Healthcare Applications