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
Mirai
Mammography-based model predicting five-year breast cancer risk from the four standard screening views, designed to be robust across hospitals and to work with or without clinical risk factors.
Mia
Commercial AI reader for screening mammography, evaluated as an additional reader in the UK's double-reading pathway; the GEMINI prospective study in Aberdeen reported extra cancers found when Mia flagged cases the readers had cleared.
Transpara
Commercial mammography AI with FDA clearance and CE marking, used as a concurrent or replacement reader in breast screening; the MASAI randomised trial in Sweden used it to triage screening reads.
Sybil
Deep-learning model that predicts an individual's risk of lung cancer over the next one to six years from a single low-dose chest CT, without radiologist annotations or clinical variables.
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.