{"count":3,"items":[{"article":null,"cancer_slugs":["pan-cancer"],"category":"multimodal","confidence":"high","developer":"Microsoft Research","hf":{"downloads":249031,"fetched_at":"2026-09-09T21:33:08Z","gated":false,"last_modified":"2025-01-14","library":"open_clip","license":"mit","likes":424,"pipeline_tag":"zero-shot-image-classification"},"kind":"foundation","license":"MIT","links":{"demo":null,"docs":null,"doi":null,"github":"https://github.com/microsoft/BiomedCLIP_data_pipeline","huggingface":"https://huggingface.co/microsoft/BiomedCLIP-PubMedBERT_256-vit_base_patch16_224","paper":"https://ai.nejm.org/doi/full/10.1056/AIoa2400640","pmid":null},"modalities":["multimodal","histopathology","radiology-xray","radiology-ct","literature"],"name":"BiomedCLIP","openness":"open-weights","regulatory_status":"research-only","release_date":"2023-03-02","settings":["basic-research","education"],"slug":"biomedclip","summary":"CLIP-style vision-language model pretrained on PMC-15M \u2014 15 million figure\u2013caption pairs from biomedical papers \u2014 with a PubMedBERT text tower and a ViT-B image tower; supports zero-shot classification and retrieval across pathology, radiology and more.","tasks":["image-text-retrieval","classification","feature-extraction","question-answering"],"url":"/ai-oncology/models/biomedclip","verified_at":"2026-09-05T22:26:00.027245","version":null},{"article":null,"cancer_slugs":["pan-cancer"],"category":"clinical-LLM","confidence":"medium","developer":"Google (Health AI Developer Foundations)","hf":{"downloads":1105085,"fetched_at":"2026-09-09T21:33:09Z","gated":"auto","last_modified":"2025-10-28","library":"transformers","license":"other","likes":1048,"pipeline_tag":"image-text-to-text"},"kind":"foundation","license":"Health AI Developer Foundations terms of use","links":{"demo":null,"docs":"https://developers.google.com/health-ai-developer-foundations/medgemma","doi":null,"github":null,"huggingface":"https://huggingface.co/google/medgemma-4b-it","paper":"https://arxiv.org/abs/2507.05201","pmid":null},"modalities":["multimodal","radiology-xray","histopathology","dermoscopy","clinical-text"],"name":"MedGemma","openness":"gated-weights","regulatory_status":"research-only","release_date":"2025-05-20","settings":["basic-research","education"],"slug":"medgemma","summary":"Open-weight medical vision-language models built on Gemma 3: the 4B variant reads chest X-rays, dermatology, ophthalmology and histopathology images alongside text; the 27B variant targets medical text reasoning. 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