Evaluating the Spatial Reasoning Capabilities of Large Multimodal Models on Chest X-Ray Anomaly Detection
Name
Skylar-Li-MIT-AIEDU-Final-Paper.pdf
Size
2.04 MB
Format
Adobe PDF
Checksum (MD5)
8b3b1d705f40c9c41efaecae945ba01a
Author(s)
Li, Linday Skylar
Date Issued
July 2025
Journal
2025 MIT AI and Education Summit
Abstract
While current results show potential in LMM-based diagnosis, it is unclear if the output of them are backed by strong spatial reasoning capabilities. To evaluate this, I provided GPT-4o with chest X-rays and asked it to return diagnoses and the coordinates of bounding boxes that surrounded any identified abnormalities on the NIH chest X-ray dataset. I find variable performance across different images in the dataset, suggesting the need for further development of the spatial reasoning capabilities of LMMs.
Persistent DSpace Link
