MultiMedEval: A Benchmark and a Toolkit for Evaluating Medical Vision-Language Models
This provides a standardized evaluation framework for researchers in medical AI, though it is incremental as it builds on existing benchmarks.
The authors introduced MultiMedEval, a benchmark and toolkit for evaluating medical vision-language models, covering six tasks across 23 datasets and 11 domains to assess generalizability.
We introduce MultiMedEval, an open-source toolkit for fair and reproducible evaluation of large, medical vision-language models (VLM). MultiMedEval comprehensively assesses the models' performance on a broad array of six multi-modal tasks, conducted over 23 datasets, and spanning over 11 medical domains. The chosen tasks and performance metrics are based on their widespread adoption in the community and their diversity, ensuring a thorough evaluation of the model's overall generalizability. We open-source a Python toolkit (github.com/corentin-ryr/MultiMedEval) with a simple interface and setup process, enabling the evaluation of any VLM in just a few lines of code. Our goal is to simplify the intricate landscape of VLM evaluation, thus promoting fair and uniform benchmarking of future models.