AI in Oncology · AI Models

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.

Vision-language foundation model for pathology: an image encoder and a text encoder trained together on 1.17 million histopathology image–caption pairs, enabling zero-shot classification and image–text retrieval without labelled slides.

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. Meant as a starting point for developers to fine-tune, not as a finished clinical product.

Three-stage LLM framework (retrieval, criterion-level matching, ranking) that matches a patient summary to clinical trials from ClinicalTrials.gov; in the paper it cut clinician screening time by more than 40% in a pilot user study.

Google's medical large language model, the first to reach expert-level scores on USMLE-style questions (86.5% on MedQA); available only through Google Cloud to selected partners, and largely succeeded by Gemini-based medical models.