Beyond the Prompt: Deploying Medical Foundation Models on Diverse Chest X-ray Populations
Keywords: multimodal foundation model, bias, zero-shot, pneumonia, ensembles
TL;DR: Presenting ways to deploy foundation models with optimized text and vision prompts for superior and equitable clinical applications across diverse populations.
Abstract: Foundation models (FMs) have shown impressive performance in medical image analysis tasks, but their deployment in real-world clinical settings, especially across diverse patient populations such as adult and pediatric cases, remains challenging. Key open questions include optimal prompting techniques and strategies for model adaptation or fine-tuning for clinical use. In this study, we evaluated different approaches for deploying FMs in clinical scenarios for diverse patient populations. We use the lightweight, embedding-based vision-language FM $\textit{MedImageInsight}$ to predict pneumonia from chest X-rays, a condition common in both adult and pediatric patients.
We observed large variation in model predictive performance depending on the chosen prompt design, highlighting the importance of text prompt design for successful zero-shot (ZS) application. On in-domain datasets, we found performance differences of up to 46% in Matthews correlation coefficient (MCC) and 56% in true positive rates across different text prompts.
By introducing text and vision embedding ensembles, we achieved substantial ZS improvements, outperforming training-based methods (fine-tuning, Linear Probe) in low-data scenarios by up to 43% for adults and 35% for pediatric populations (MCC). This ensembling strategy also promotes resource-efficient equitable clinical use by supporting diverse demographic subgroups, achieving MCC improvements of 6% by sex, 17% by age, and 10% by race compared to Linear Probe.
Primary Subject Area: Foundation Models
Secondary Subject Area: Transfer Learning and Domain Adaptation
Paper Type: Both
Registration Requirement: Yes
Submission Number: 78
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