All Competitors

Lightweight 7B vision-language foundation model from Microsoft Research, released research-only under the Microsoft Research License, that generates radiology findings from chest X-rays.

A multimodal large language model for 3D medical imaging, handling retrieval, report generation, VQA, positioning, and segmentation on CT volumes.

Large-scale chest X-ray vision-language pretraining model that learns image-report alignment for zero-shot and few-shot radiograph classification.

A biomedical vision-language model trained with contrastive learning on 1.6M image-caption pairs (PMC-OA) mined from PubMed Central open-access articles.

Prompt-based medical vision-language pretraining that unifies fusion-encoder and dual-encoder architectures, handling image-only, text-only, and image-text inputs in one model.

Self-supervised medical vision-and-language pretraining via multi-modal masked autoencoders that reconstruct masked image patches and text tokens.

Knowledge-enhanced medical vision-and-language pre-training framework that aligns, reasons over, and learns from structured medical knowledge for radiology image-text tasks.

A CLIP model fine-tuned on ROCO medical image-caption pairs to provide a medical-domain visual encoder for tasks such as medical visual question answering.