AI in Oncology · AI Models
A researcher-grade catalog of AI models, datasets and open data needs in oncology — every card structured, sourced and dated.
Filter by task, data type, cancer, availability and regulatory status. Each card follows one model-card standard and links to Hugging Face, code, papers and the datasets it was trained or tested on. { } export JSON
BiomedBERT (PubMedBERT)
BERT pretrained from scratch on PubMed abstracts and PMC full text with a biomedical vocabulary; the workhorse encoder for named-entity recognition, relation extraction and classification over oncology literature and reports.
BiomedCLIP
CLIP-style vision-language model pretrained on PMC-15M — 15 million figure–caption pairs from biomedical papers — with a PubMedBERT text tower and a ViT-B image tower; supports zero-shot classification and retrieval across pathology, radiology and more.
TxGNN
Graph neural network for zero-shot drug repurposing that scores drug–disease indications and contraindications over a medical knowledge graph of 17,080 diseases, including ones with no approved treatment.
Cards follow the cancer3.ai model-card standard: AI_MODEL_CARD_STANDARD.md. Corrections and new entries: contact the editorial team; every fact needs a public source.
This page is educational — it is not medical advice and does not replace consultation with an oncologist. Diagnostic and treatment decisions are made solely by specialist physicians.