All Competitors

Multi-sequence spine MRI foundation model with DINOv3 encoders, supporting condition classification, pathology localization, and report generation.

3D vision-language foundation model for abdominal CT, pretrained on scans, radiology reports, and EHR codes for zero-shot interpretation.

Generalist neuroimaging vision foundation model pretrained on 5.24M clinical MRI and CT volumes for radiologic diagnosis and report generation.

Vision-language encoders for chest CT that align 3D volumes with radiology reports using contrastive, report-generation, and masked-image objectives.

Generalist medical multimodal LLM for image understanding, visual question answering, and report generation across twelve-plus imaging modalities.

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

Chest X-ray vision-language model that drafts the findings section of a radiology report, at 7B parameters small enough to run on a single GPU.

Text-to-image diffusion model that generates synthetic medical images across imaging modalities and organs to augment scarce clinical training data.

Multimodal vision-text foundation model for brain CT and MRI, pretrained on roughly 10 million image-report pairs to act as a clinical copilot.

Bilingual Arabic-English medical multimodal model built on Llama 3.1 for radiology, CT, and histology image understanding and question answering.

Slide-level pathology foundation model turning whole-slide images into reusable embeddings for classification, retrieval, and report generation.

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

Multimodal large language model that interprets 12-lead electrocardiogram images, answering open-ended clinical questions and generating ECG reports.

Multimodal ECG-language model aligning 12-lead waveforms with clinical report text for conversational cardiac diagnosis and report generation.

Medical vision-language model trained on the MedTrinity-25M dataset, answering questions and generating text about radiology and histology images.

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

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

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

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.

Multimodal pathology assistant that answers questions about histology and cytology images, pairing the PathCLIP vision encoder with a Vicuna-13B LLM.

Medical vision-language pretraining unifying fusion-encoder and dual-encoder designs, handling image-only, text-only, and paired inputs in one model.