All Competitors

A medical vision-language model that accepts visual-referring multimodal input and produces pixel-grounded multimodal output, jointly answering and segmenting medical images.

A 4.2B-parameter lightweight biomedical vision-language assistant built on Phi-2 that outperforms larger LLaVA-Med models on medical visual question answering.

A CLIP-based visual-language foundation model for multi-modal ophthalmic imaging, enabling zero-shot disease detection across 11 modalities including fundus, OCT, and slit-lamp.

Open multimodal clinical foundation model that jointly reasons over medical images, ECG time-series, and text reports, trained with domain-aware reinforcement learning.

Universal foundation model that jointly generates diagnostic text and segments the corresponding targets across ten biomedical imaging modalities.

A reinforcement-learning-enhanced general medical vision-language model that adds step-by-step reasoning for medical image diagnosis and visual question answering.

A 2B-parameter medical vision-language model that uses reinforcement learning (GRPO) to produce explicit, human-interpretable reasoning for radiology visual question answering.

Medical large vision-language model unifying image comprehension and generation in one autoregressive framework via heterogeneous LoRA knowledge adaptation.

Grounded multimodal large language model for endoscopic surgery, supporting visual dialogue, region-based question answering, and bounding-box grounding across surgical scene understanding tasks.

A vision-language foundation model for precision oncology that pretrains on 50M pathology images and 1B text tokens via unified masked modeling.

Biomedical multimodal LLM with pixel-level insight, combining visual question answering, pixel-grounded prompts, and segmentation via a mixture-of-experts design.

A bilingual (Arabic-English) bio-medical large multimodal model built on Llama 3.1 for medical image understanding and clinical text conversation.

Region-aware bilingual (Chinese-English) medical multimodal LLM that handles image- and region-level vision-language tasks across eight imaging modalities.

Open-source, lightweight generalist vision-language foundation model for diverse biomedical imaging and text tasks.

A medical multimodal large language model pretrained on the 25M-image MedTrinity-25M dataset, achieving state-of-the-art accuracy on biomedical visual question answering.

A multimodal vision-language copilot for human pathology that analyzes histology images and answers diverse pathology queries in natural language.

A family of open medical multimodal LLMs (7B and 34B) trained on PubMedVision, a 1.3M-sample medical VQA dataset distilled from PubMed image-text pairs.

Microsoft Research multimodal LLM for grounded chest X-ray report generation, localizing each described finding with bounding boxes on the image.

A multimodal generalist foundation model for ophthalmic imaging, self-supervised on 2.78M images across 11 modalities for diagnosis, prognosis, and visual question answering.

A 40B-parameter generalist medical vision-language foundation model spanning radiology, pathology, dermatology, retinography, and endoscopy.

A lightweight mixture-of-experts medical vision-language model that routes between domain-specific experts for VQA and image classification while activating only 30-50% of parameters.

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

An instruction-tuned vision-language foundation model from Stanford for interpreting and summarizing chest X-rays across eight clinical task types.

The first Chinese medical large vision-language model, pairing a pretrained ViT with an LLM to interpret medical images and answer clinical questions in Chinese.