arXiv Computer Vision September 7, 2026 By Xin Chen, Dongliang Xu, Cunhao Zhu, Xudong Luo, Haoyang Lyu, Xiaoxiao Sun, Serena Yeung-Levy, Yue Yao

Solve the Missing First Step: Can VLMs Standardize Raw Heterogeneous Medical Data?

Read the original on arXiv Computer Vision →

The paper introduces the Medical Data Standardization Benchmark (MDS‑Bench), which evaluates vision‑language models (VLMs) on their ability to process raw, heterogeneous medical data. Models must identify source formats, convert raw images into VLM‑compatible inputs, extract relevant text, and organize the results into structured image‑text pairs. Experiments show that even the top VLM, Gemini 3 Flash, achieves only a 48.6% end‑to‑end success rate, underscoring the challenge of raw data standardization in clinical settings.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

Hugging Face Trending Papers

Jul 6

Solve the Missing First Step: Can VLMs Standardize Raw Heterogeneous Medical Data?

As vision-language models (VLMs) are increasingly applied to medical AI, existing benchmarks mainly focus on evaluating their diagnosis ability over given medical images and texts, implicitly assuming that standardized medical images, texts or question-answer pairs are already prepared. However, this assumption does not hold when we apply VLMs in real clinical practice, where medical data is often raw, heterogeneous, and fragmented across different sources.

llms multimodal benchmarks

arXiv AI

Jun 9

MedVision: Benchmarking Quantitative Medical Image Analysis

arXiv:2511. 18676v2 Announce Type: replace-cross Abstract: Current vision-language models (VLMs) in medicine are primarily designed for categorical question answering (e.

By Yongcheng Yao, Yongshuo Zong, Raman Dutt, Yongxin Yang, Sotirios A Tsaftaris, Timothy Hospedales

llms nlp fine-tuning multimodal benchmarks

arXiv AI

Aug 14

Can Generalist Vision Language Models (VLMs) Rival Specialist Medical VLMs? Benchmarking and Strategic Insights

arXiv:2506. 17337v5 Announce Type: replace-cross Abstract: Vision Language Models (VLMs) have shown promise in automating image diagnosis and interpretation in clinical settings.

By Yuan Zhong, Ruinan Jin, Qi Dou, Xiaoxiao Li

llms fine-tuning benchmarks

arXiv AI

Jul 2

Text Over Image: Auditing Multimodal Robustness in Synthetic Medical Image Detection

arXiv:2606. 25375v2 Announce Type: replace-cross Abstract: With the rapid adoption of generative AI, synthetic medical images pose growing risks, including diagnostic deception and insurance fraud.

By Ching-Hao Chiu, Hao-Wei Chung, Gelei Xu, Xueyang Li, Pin-Yu Chen, John Kheir, Meysam Ghaffari, Carlos Morato, Ahmed Abbasi, Yiyu Shi

llms multimodal benchmarks

arXiv Computer Vision

Aug 25

MedReaMM: Evaluating Large Multimodal Models on Expert-Level Clinical Diagnostic Synthesis

arXiv:2608.22323v1 Announce Type: new Abstract: The application of Large Language Models (LLMs) to diagnostic decision-making has garnered growing interest. However, existing benchmarks largely focus...

By Lai Wei, Yuchao Chen, Zhenbiao Cao, Xiaojin Zhang, Zhongyu Wei, Bangting Wang, Wei Chen, Xiang Bai

llms multimodal benchmarks

arXiv Computer Vision

Aug 25

Expert-level vision-language foundation model for real-world radiology and comprehensive evaluation

arXiv:2409.16183v2 Announce Type: replace Abstract: Radiology is a vital and complex component of modern clinical workflow and covers many tasks. Recently, vision-language (VL) foundation models in m...

By Xiaohong Liu, Guoxing Yang, Yulin Luo, Jiaji Mao, Xiang Zhang, Haibo Wang, Zhiyang He, Ming Gao, Shanghang Zhang, Jun Shen, Guangyu Wang

multimodal benchmarks

One email a morning, machine-written

Related stories

Solve the Missing First Step: Can VLMs Standardize Raw Heterogeneous Medical Data?

MedVision: Benchmarking Quantitative Medical Image Analysis

Can Generalist Vision Language Models (VLMs) Rival Specialist Medical VLMs? Benchmarking and Strategic Insights

Text Over Image: Auditing Multimodal Robustness in Synthetic Medical Image Detection

MedReaMM: Evaluating Large Multimodal Models on Expert-Level Clinical Diagnostic Synthesis

Expert-level vision-language foundation model for real-world radiology and comprehensive evaluation