MIMIC CXR
Progress Over Time
Interactive timeline showing model performance evolution on MIMIC CXR
MIMIC CXR Leaderboard
| Context | Cost | License | ||||
|---|---|---|---|---|---|---|
| 1 |  Google | 4B | — | — |
What is MIMIC CXR?
MIMIC-CXR is a large publicly available dataset of chest radiographs with free-text radiology reports. Contains 377,110 images corresponding to 227,835 radiographic studies from 65,379 patients at Beth Israel Deaconess Medical Center. The dataset is de-identified and widely used for medical imaging research, automated report generation, and medical AI development.
MIMIC CXR is a multimodal benchmark evaluating models on multimodal, healthcare, and vision tasks. LLM Stats tracks 1 models on this benchmark, scored on a 0–1 scale. The current average is 0.9, with the leader at 0.9.
Compare leaders on the best AI for multimodal, best AI for healthcare and best AI for vision leaderboards.
Current leaders
MedGemma 4B IT from Google currently leads the MIMIC CXR leaderboard with a score of 0.889 across 1 evaluated AI models.
Source paper
- Title
- MIMIC-CXR-JPG, a large publicly available database of labeled chest radiographs
- Authors
- Alistair E. W. Johnson, Tom J. Pollard, Nathaniel R. Greenbaum, Matthew P. Lungren, and 6 others
- Published
- arXiv
- 1901.07042
Abstract
Chest radiography is an extremely powerful imaging modality, allowing for a detailed inspection of a patient's thorax, but requiring specialized training for proper interpretation. With the advent of high performance general purpose computer vision algorithms, the accurate automated analysis of chest radiographs is becoming increasingly of interest to researchers. However, a key challenge in the development of these techniques is the lack of sufficient data. Here we describe MIMIC-CXR-JPG v2.0.0, a large dataset of 377,110 chest x-rays associated with 227,827 imaging studies sourced from the Beth Israel Deaconess Medical Center between 2011 - 2016. Images are provided with 14 labels derived from two natural language processing tools applied to the corresponding free-text radiology reports. MIMIC-CXR-JPG is derived entirely from the MIMIC-CXR database, and aims to provide a convenient processed version of MIMIC-CXR, as well as to provide a standard reference for data splits and image labels. All images have been de-identified to protect patient privacy. The dataset is made freely available to facilitate and encourage a wide range of research in medical computer vision.
FAQ
Common questions about the MIMIC CXR benchmark and leaderboard.