# MultiMedEval: A Benchmark and a Toolkit for Evaluating Medical Vision-Language Models

arXiv: https://arxiv.org/abs/2402.09262 · HTML: https://mlsanity.com/paper/2402.09262/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- Placed in Medical vision-language models, one of 16 directions in Multimodal LLMs / vision-language models.
- Builds on 19 of its 39 references that are in the map.
- First posted on arXiv 14 Feb 2024.

## Placed in

- Multimodal LLMs / vision-language models / [Medical vision-language models](https://mlsanity.com/mllm/medical-vision-language-models/)

## Builds on (most cited first, 19 of 19)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [Flamingo: a Visual Language Model for Few-Shot Learning](https://mlsanity.com/paper/2204.14198/) (2022)
- [Skin Lesion Analysis Toward Melanoma Detection 2018: A Challenge Hosted by the International Skin Imaging Collaboration (ISIC)](https://mlsanity.com/paper/1902.03368/) (2019)
- [The Liver Tumor Segmentation Benchmark (LiTS)](https://mlsanity.com/paper/1901.04056/) (2019)
- [SLAKE: A Semantically-Labeled Knowledge-Enhanced Dataset for Medical Visual Question Answering](https://mlsanity.com/paper/2102.09542/) (2021)
- [PMC-VQA: Visual Instruction Tuning for Medical Visual Question Answering](https://mlsanity.com/paper/2305.10415/) (2023)
- [CheXbert: Combining Automatic Labelers and Expert Annotations for Accurate Radiology Report Labeling Using BERT](https://mlsanity.com/paper/2004.09167/) (2020)
- [RadGraph: Extracting Clinical Entities and Relations from Radiology Reports](https://mlsanity.com/paper/2106.14463/) (2021)
- [Towards Generalist Biomedical AI](https://mlsanity.com/paper/2307.14334/) (2023)
- [BiomedGPT: A Generalist Vision-Language Foundation Model for Diverse Biomedical Tasks](https://mlsanity.com/paper/2305.17100/) (2023)
- [Towards Generalist Foundation Model for Radiology by Leveraging Web-scale 2D&3D Medical Data](https://mlsanity.com/paper/2308.02463/) (2023)
- [Lessons from Natural Language Inference in the Clinical Domain](https://mlsanity.com/paper/1808.06752/) (2018)
- [PAD-UFES-20: a skin lesion dataset composed of patient data and clinical images collected from smartphones](https://mlsanity.com/paper/2007.00478/) (2020)
- [VinDr-Mammo: A large-scale benchmark dataset for computer-aided diagnosis in full-field digital mammography](https://mlsanity.com/paper/2203.11205/) (2022)
- [Overcoming Data Limitation in Medical Visual Question Answering](https://mlsanity.com/paper/1909.11867/) (2019)
- [The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions](https://mlsanity.com/paper/1803.10417/) (2018)
- [RadAdapt: Radiology Report Summarization via Lightweight Domain Adaptation of Large Language Models](https://mlsanity.com/paper/2305.01146/) (2023)
- [Pathological Visual Question Answering](https://mlsanity.com/paper/2010.12435/) (2020)

## Built on by (most cited first, 29 of 29)

- [Advancing Multimodal Medical Capabilities of Gemini](https://mlsanity.com/paper/2405.03162/) (2024)
- [MMed-RAG: Versatile Multimodal RAG System for Medical Vision Language Models](https://mlsanity.com/paper/2410.13085/) (2024)
- [MME-Survey: A Comprehensive Survey on Evaluation of Multimodal LLMs](https://mlsanity.com/paper/2411.15296/) (2024)
- [CARES: A Comprehensive Benchmark of Trustworthiness in Medical Vision Language Models](https://mlsanity.com/paper/2406.06007/) (2024)
- [MediConfusion: Can you trust your AI radiologist? Probing the reliability of multimodal medical foundation models](https://mlsanity.com/paper/2409.15477/) (2024)
- [MMedPO: Aligning Medical Vision-Language Models with Clinical-Aware Multimodal Preference Optimization](https://mlsanity.com/paper/2412.06141/) (2024)
- [AgroBench: Vision-Language Model Benchmark in Agriculture](https://mlsanity.com/paper/2507.20519/) (2025)
- [DrVD-Bench: Do Vision-Language Models Reason Like Human Doctors in Medical Image Diagnosis?](https://mlsanity.com/paper/2505.24173/) (2025)
- [3DReasonKnee: Advancing Grounded Reasoning in Medical Vision Language Models](https://mlsanity.com/paper/2510.20967/) (2025)
- [AVA: Attentive VLM Agent for Mastering StarCraft II](https://mlsanity.com/paper/2503.05383/) (2025)
- [SilVar-Med: A Speech-Driven Visual Language Model for Explainable Abnormality Detection in Medical Imaging](https://mlsanity.com/paper/2504.10642/) (2025)
- [MME-CoF-Pro: Evaluating Reasoning Coherence in Video Generative Models with Text and Visual Hints](https://mlsanity.com/paper/2603.20194/) (2026)
- [Can Generalist Vision Language Models (VLMs) Rival Specialist Medical VLMs? Benchmarking and Strategic Insights](https://mlsanity.com/paper/2506.17337/) (2025)
- [Uncertainty-Driven Expert Control: Enhancing the Reliability of Medical Vision-Language Models](https://mlsanity.com/paper/2507.09209/) (2025)
- [PARC: A Quantitative Framework Uncovering the Symmetries within Vision Language Models](https://mlsanity.com/paper/2506.14808/) (2025)
- [6 Fingers, 1 Kidney: Natural Adversarial Medical Images Reveal Critical Weaknesses of Vision-Language Models](https://mlsanity.com/paper/2512.04238/) (2025)
- [Artificial Intelligence for Biomedical Video Generation](https://mlsanity.com/paper/2411.07619/) (2024)
- [VSF-Med:A Vulnerability Scoring Framework for Medical Vision-Language Models](https://mlsanity.com/paper/2507.00052/) (2025)
- [MedBLINK: Probing Basic Perception in Multimodal Language Models for Medicine](https://mlsanity.com/paper/2508.02951/) (2025)
- [MarineEval: Assessing the Marine Intelligence of Vision-Language Models](https://mlsanity.com/paper/2512.21126/) (2025)
- [BUZZY: Contrastive Scoring to Mitigate Text-Induced Bias in Multimodal Multiple-Choice QA](https://mlsanity.com/paper/2603.28026/) (2026)
- [Doctor Sun: A Bilingual Multimodal Large Language Model for Biomedical AI](https://mlsanity.com/paper/2508.08270/) (2025)
- [Process Supervision for Chain-of-Thought Reasoning via Monte Carlo Net Information Gain](https://mlsanity.com/paper/2603.17815/) (2026)
- [When Minor Edits Matter: LLM-Driven Prompt Attack for Medical VLM Robustness in Ultrasound](https://mlsanity.com/paper/2603.21047/) (2026)
- [CXR-ContraBench: Benchmarking Negated-Option Attraction in Medical VLMs](https://mlsanity.com/paper/2605.05810/) (2026)
- [Med-R2: An Adversarial Benchmark for Evidence-Grounded Reasoning in Medical VLMs](https://mlsanity.com/paper/2605.24492/) (2026)
- [EasyLens: A Training-Free Plug-and-Play Subtle-Lesion Representation Amplifier for Medical Vision-Language Models](https://mlsanity.com/paper/2606.06379/) (2026)
- [Authority-Preserving Evaluation of Medical Vision-Language Assistants](https://mlsanity.com/paper/2609.22302/) (2026)
- [Reliability Stress Tests and Decision-Time Routing for Chest X-ray Vision-Language Models](https://mlsanity.com/paper/2610.02270/) (2026)

## Abstract (arXiv)

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.