Abstract
Medical image diagnosis is challenging because many diseases resemble normal anatomy and exhibit substantial interpatient variability. Clinicians routinely rely on comparative diagnosis, such as referencing cross-patient healthy control images to identify subtle but clinically meaningful abnormalities. Although healthy reference images are abundant in practice, existing medical vision-language models (VLMs) primarily operate in a single-image or single-series setting and lack explicit mechanisms for comparative diagnosis. This work investigates whether incorporating clinically motivated comparison can enhance VLM performance. We show that providing VLMs with both a query image and a matched healthy reference image, accompanied by cross-patient comparative prompts, significantly improves diagnostic performance. This performance can be further augmented by lightweight supervised fine-tuning (SFT) on a small amount of data. At the same time, we evaluate multiple strategies for selecting r