Models in this group take a chest radiograph, some with text prompts alongside. Each table gives three hardware tiers — Minimum, the smallest configuration on which the model runs correctly; Medium, the usual production configuration; and High, a configuration sized for peak volume. The rate is what one server of that tier processes per hour. Use these figures for initial sizing only. Before production we benchmark your own data to confirm accuracy, latency, throughput and cost.
TorchXRayVision model zoo
Vendor: Mila / Machine Learning and Medicine Lab (Cohen et al.)
What it does: scores eighteen common findings on a chest radiograph, fast and on modest hardware. Its published accuracy is cross-dataset validated, which makes it lower than the competition and more likely to hold on your data.
AUC 0.78–0.85 across 18 findings, explicitly cross-dataset validated so the numbers are modest but honest (independent).
RAD-DINO
Vendor: Microsoft Research
What it does: turns a chest radiograph into an image representation without using report text to learn, and is best in class for the classifiers and report models built on top of it.
Best-in-class CXR image encoder for downstream classification and report generation, trained without text supervision (independent).
CXR Foundation / ELIXR-B
Vendor: Google Health
What it does: scores findings zero-shot or trains a classifier from a small labelled set, at AUC around 0.89–0.93 on the common findings.
Zero-shot AUC 0.89 cardiomegaly, 0.93 pleural effusion, 0.88 pneumonia on CheXpert-style labels (developer-reported). Legacy model — MedSigLIP is the current recommendation.
CheXzero
Vendor: Stanford / CheXzero authors
What it does: scores pathology from a text prompt alone, so a new finding needs a phrase rather than a labelled dataset.
Task-dependent across chest X-ray pathology labels; no single universal accuracy.
MedCLIP
Vendor: MedCLIP authors
What it does: indexes chest radiographs against text for zero-shot classification and retrieval, useful where your finding list changes faster than you can retrain.
Task-dependent across zero-shot X-ray classification and retrieval.
CXR-CLIP
Vendor: CXR-CLIP authors
What it does: pairs radiographs with text for classification and retrieval, and is light enough to index a large archive quickly.
Task-dependent across chest X-ray classification and retrieval benchmarks.
MedKLIP
Vendor: MedKLIP authors
What it does: adds an external medical knowledge source to the image-text pairing, which improves diagnosis on the findings where plain text pairing is weakest.
Task-dependent across chest X-ray diagnostic datasets.
KAD
Vendor: KAD authors
What it does: brings structured medical knowledge into chest X-ray diagnosis, returning disease probabilities with the knowledge trail behind them.
Task-dependent across chest X-ray diagnosis benchmarks.
AFLoc
Vendor: AFLoc authors
What it does: localises the pathology on the image as well as scoring it, so a reader can check the claim against the pixels in seconds.
Task-dependent across localisation and classification benchmarks.