Analysis of Blood Report Images Using General Purpose Vision-Language Models
This work addresses the challenge of health literacy for individuals by providing preliminary analysis of blood reports, though it is incremental as it applies existing VLMs to a new medical domain.
The study tackled the problem of individuals struggling to interpret blood reports by evaluating three general-purpose Vision-Language Models (VLMs) on a dataset of 100 blood report images, finding them practical and promising for developing patient-facing tools to improve health literacy.
The reliable analysis of blood reports is important for health knowledge, but individuals often struggle with interpretation, leading to anxiety and overlooked issues. We explore the potential of general-purpose Vision-Language Models (VLMs) to address this challenge by automatically analyzing blood report images. We conduct a comparative evaluation of three VLMs: Qwen-VL-Max, Gemini 2.5 Pro, and Llama 4 Maverick, determining their performance on a dataset of 100 diverse blood report images. Each model was prompted with clinically relevant questions adapted to each blood report. The answers were then processed using Sentence-BERT to compare and evaluate how closely the models responded. The findings suggest that general-purpose VLMs are a practical and promising technology for developing patient-facing tools for preliminary blood report analysis. Their ability to provide clear interpretations directly from images can improve health literacy and reduce the limitations to understanding complex medical information. This work establishes a foundation for the future development of reliable and accessible AI-assisted healthcare applications. While results are encouraging, they should be interpreted cautiously given the limited dataset size.