Teaching pack: AI & Computation

FDA-cleared digital pathology risk tools (ArteraAI), radiology triage and screening models, pathology and radiology foundation models, multimodal patient-level models, AI-driven target discovery and ADC design, and LLM-based trial matching and tumour-board support.

• AI auto-contouring and adaptive planning: Software that draws organs and tumours on scans automatically, saving hours per patient and making daily plan adaptation practical.
• AI compute and model platforms for oncology: AI compute platforms are the GPUs, model libraries, and cloud services that pathology, radiology, and drug-design AI run on.
• AI in radiology: Software that reads scans alongside radiologists, catching cancers earlier and predicting who is at risk.
• AI trial matching & clinical decision support: Software, increasingly LLM-based, that reads a patient's record and finds trials or guideline options they qualify for.
• AI-driven drug & target discovery: Using machine learning to pick targets, design molecules and antibodies, and predict which ADC will work.
• Aidoc CARE (clinical radiology foundation model): Aidoc CARE is a single foundation model behind many FDA-cleared triage alerts in emergency radiology.
• AlphaFold 3: Predicts the 3D shape of proteins together with DNA, RNA, small molecules and antibodies, the starting point for much modern drug design.
• AlphaGenome: Reads a million letters of DNA at once and predicts how a mutation changes gene regulation, splicing and chromatin.
• AlphaMissense: Scored all 71 million possible single-letter protein changes in humans as likely harmful or benign.
• Atlas (Aignostics, Mayo Clinic, Charité): Atlas is a pathology foundation model trained on 1.2 million slides from two of the world's largest hospitals.

1. What is the first FDA-cleared AI pathology tool that predicts treatment benefit, and in which cancer?
   Answer

   ArteraAI Prostate (de novo authorisation, August 2025) predicts prognosis and benefit from short-term androgen deprivation with radiotherapy in localised prostate cancer.
2. What are foundation models in pathology and what have they enabled?
   Answer

   Very large self-supervised models trained on millions of slides (Virchow, UNI, CONCH, Prov-GigaPath) that adapt to many tasks; they predict molecular status from H&E and power FDA-cleared tools such as ArteraAI Prostate and ArteraAI Breast.