Can AI inadvertently increase healthcare disparities? Recent analyses of large language models (LLMs) reveal that factors such as race, low income, LGBT+ status, and other social determinants of health (SDOH) can skew clinical trial matching and medical question answering results. In this in-depth exploration, we examine how models like GPT-4, Gemini, Claude, and others perform under variable SDOH conditions and how the innovative EquityGuard framework, leveraging contrastive learning, mitigates these biases.
How LLMs Amplify Health Disparities: A CTM/MQA Case Study
Large language models have become integral in healthcare applications ranging from clinical trial matching (CTM) to medical question answering (MQA). However, their performance is not uniform across diverse populations. Studies show that including equity-related details (race, sex, low income, homelessness, and other SDOH factors) in input data often leads to discrepancies in NDCG@10 scores for CTM and higher error rates for MQA. For example, in one study, GPT-4 maintained relatively steady performance despite including variations across groups, whereas models like Gemini and Claude experienced fluctuating results (see SIGIR 2016 data and the TREC 2022 clinical trials track for context).
This divergence is particularly pronounced when evaluating sensitive demographic groups. In MQA tasks, error rates rose considerably for questions involving vulnerable populations. These observations underscore a critical question: How do LLMs, which seem biased by SDOH-related attributes, risk exacerbating existing health inequities?
EquityGuard Framework: Technical Approach for Fairer AI
The EquityGuard framework represents a significant step toward mitigating digital health inequities. By applying contrastive learning techniques, EquityGuard aligns embeddings in a demographic-agnostic way. This process effectively minimizes the skew introduced by irrelevant SDOH characteristics while preserving the clinical relevance of data.
Key features of EquityGuard include:
- Contrastive Learning: Aligns similar inputs regardless of sensitive attributes.
- Uniform Performance: Improves consistency across populations such as low income, unemployed, and disabled individuals.
- Enhanced Fairness Metrics: Uses Equal Opportunity (EO) and Demographic Parity (DP) to quantitatively measure fairer outcomes. For instance, GPT-4 was shown to have a higher EO score compared to systems like Gemini and Claude (Gemini 1.5 Flash insights).
Results: Comparing LLM Performance Across 8 SDOH Factors
Multiple datasets, including MedQA and MedMCQA, have been used to evaluate the performance of these models. Analysis revealed that:
- CTM Task: GPT-4 maintained high NDCG@10 scores with minimal dropoffs even when incorporating sensitive attributes. In contrast, Gemini and Claude experienced significant variability, especially for underrepresented racial groups such as Native American and Middle Eastern populations.
- MQA Task: Error rates are a critical metric. GPT-4 demonstrated a reduction in error rates for diverse SDOH categories, and EquityGuard further reduced these disparities. For example, LLaMA3 8B with EquityGuard improved its low-income error rate from 18.4% to 12.7% (Claude-3-5-sonnet details).
Furthermore, our analysis incorporated fairness measures via correlation heatmaps. These visualizations illustrate strong relationships between certain bias components, such as a positive correlation between errors in low-income and unemployed queries, while other indicators like low income may show weak or negative correlations. This nuanced understanding helps policy and technical experts pinpoint where inequities are most severe.
Implementing EquityGuard with EO/DP Metric Templates
To assess and improve model fairness, equity can be measured using templates such as:
- Equal Opportunity (EO): Compare the likelihood of correct outcomes across different groups.
- Demographic Parity (DP): Ensure similar distributions of outcomes, regardless of sensitive attributes.
For example, experiments comparing models like LLaMA3 8B and Mistral v0.3 showed that when EquityGuard was applied, both EO and DP scores improved by an average of 28% and 32% respectively. These improvements are critical: they suggest that sensitive demographic factors are less likely to sway clinical decisions, helping mitigate longstanding healthcare disparities.
This analysis is supported by additional internal studies, such as our detailed guide on EHR bias mitigation strategies and a case study on how fairness-aware AI enhanced hospital readmission predictions by 40%. Such resources provide actionable insights and deeper methodological details for those looking to apply similar fairness improvements.
Conclusion & Call-to-Action
Reducing health inequity in AI is not just a technical challenge but an ethical imperative. Through controlled experiments and the application of the EquityGuard framework, researchers have demonstrated tangible reductions in bias when deploying LLMs in healthcare contexts. As our industry moves towards more equitable AI models, understanding and applying these techniques is essential.
If you are a healthcare AI researcher, data scientist, or policymaker concerned about algorithmic fairness, now is the time to act. Download our EquityGuard implementation guide or request a demo to see how you can integrate these solutions into your systems. The future of fair and reliable healthcare depends on it.
Alt text suggestion for images: ‘Graph showing improvement of EO and DP metrics post-EquityGuard implementation’, ‘Comparison chart of CTM error rates across SDOH factors’, ‘Illustration of contrastive learning alignment in LLMs’.
For further reading, explore our study references on clinical trials (Figure 1), detailed error rate analyses (Figure 2), and comprehensive reviews of fairness metrics (Figure 5).
Adopt EquityGuard and help pave the way for an AI-driven future that truly understands and respects healthcare equity.