All Competitors

Tri-modal protein language model aligning sequence, structure, and text in one embedding space for natural-language search over billions of proteins.

Multimodal protein language model extending ESM-2 and SaProt with a Structure Adapter over residue torsion angles for protein function prediction.

ECG foundation model that learns 12-lead waveform representations by contrastively aligning each recording with machine-generated cardiological text.

Vision-language foundation model pre-trained on screening mammogram-report pairs to improve data efficiency and robustness in breast cancer detection.

Ophthalmic imaging foundation model pretrained on 2.78M images across 11 modalities for diagnosis, prognosis, and visual question answering.

Universal brain lesion segmentation for multi-modal brain MRI, using a Mixture of Modality Experts to span diverse modalities and lesion types.

Diffusion-based structure prediction model for biomolecular complexes, spanning proteins with DNA, RNA, small molecules, ions, and modified residues.

CLIP-style vision-language model for echocardiography, pretrained on echo videos and cardiologist reports for zero-shot cardiac interpretation.

Conversational agent that answers plain-language questions about DNA, RNA, and protein sequences by coupling a genomics encoder to a language model.

Family of medical multimodal models built on Gemini, adding uncertainty-guided web search, custom modality encoders, and long-context EHR reasoning.

Generalist medical vision-language foundation model with 40B parameters, spanning radiology, pathology, dermatology, retinography, and endoscopy.

Lightweight mixture-of-experts medical vision-language model routing visual question answering and image classification to domain-specific experts.

Multimodal large language model for 3D medical imaging that handles report generation, visual question answering, and segmentation on CT volumes.

Deep network that predicts structures of full biological assemblies: proteins, nucleic acids, small molecules, metals, and covalent modifications.

Instruction-tuned vision-language foundation model for chest X-ray interpretation, with 8 billion parameters spanning eight clinical task types.

Vision foundation model for the tree of life, trained on TreeOfLife-10M for zero-shot species classification of plants, animals, and fungi.

Radiology-specific multimodal LLM that generates the findings section of a chest X-ray report from a frontal image, pairing RAD-DINO with Vicuna-7B.

Promptable 3D foundation model for volumetric CT segmentation, covering over 200 anatomical categories through point, box, and free-text prompts.

Chinese medical vision-language model pairing a Vision Transformer with an LLM to caption medical images and answer clinical questions in Chinese.

Chest X-ray vision-language model that generates free-text radiology reports, pairing a CXR-specific image encoder with a 7B LLaMA-2 language model.

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

Abdominal CT segmentation model driven by CLIP text embeddings, covering 25 organs and 6 tumor types with zero-shot extension to new categories.

Vision-language pre-training framework for universal brain MRI diagnosis, learning from imaging-report pairs to cover more than ten brain diseases.

Radiology foundation model that reads interleaved 2D and 3D scans with text for diagnosis, visual question answering, and report generation.