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Train one AI on scans, pathology slides, genomics and treatment outcomes pooled across patients, including completed phase 3 trials, so it can predict which treatment will work for a new patient. Pathology and radiology models already exist separately; combining them with genomic and trial outcome data is the untested step.
Train one AI on scans, slides, genomics and outcomes pooled across patients, including completed phase 3 trials, so that it can predict, for a new patient, which treatment will work. Pathology and radiology foundation models exist separately; combining them with genomic and clinical data, as Tempus AI, Owkin and CanSim-style efforts are doing, is the next step, and ArteraAI showed that single-modality models can be predictive. Adding outcomes from randomised trials would allow causal treatment-effect estimation, for example TROP2 ADC versus chemotherapy. The test is to train on completed phase 3 datasets with sponsors, validate on held-out trials and then run a prospective biomarker-stratified trial. With preclinical evidence, it addresses the bottlenecks Data silos and AI that is built but not validated or deployed.
Two technology pages and one idea page on OnCo cite this paper by its DOI; this record gives the citation a page of its own so a reader can follow it without leaving OnCo. Read the abstract above alongside the citing pages listed under Related; the record was created automatically from the Europe PMC entry and its figures have not been checked by hand.
The shape of nearly every protein is now available to any researcher in seconds instead of years, which shortens the path from a cancer target to a designed molecule. It does not by itself produce drugs: binding pockets, dynamics and cellular context still need experiment.
Shares A pre-competitive consortium to train a shared multimodal cancer foundation model, Owkin, Pathology & radiology foundation models, AI that is built but not validated or deployed.
Shares Artera, AI in the oncology clinic: from narrow cleared tools to multimodal decision support, Pathology & radiology foundation models, AI in oncology roadmap: pattern readers → foundation models → agents in the workflow.
Shares A pre-competitive consortium to train a shared multimodal cancer foundation model, AI in the oncology clinic: from narrow cleared tools to multimodal decision support, Pathology & radiology foundation models, AI in oncology roadmap: pattern readers → foundation models → agents in the workflow.
Shares Owkin, Pathology & radiology foundation models, AI in oncology roadmap: pattern readers → foundation models → agents in the workflow, AI that is built but not validated or deployed.
Shares Towards a general-purpose foundation model for computational pathology, Pathology & radiology foundation models, AI in oncology roadmap: pattern readers → foundation models → agents in the workflow.
Shares Tempus AI, AI trial matching & clinical decision support, AI in oncology roadmap: pattern readers → foundation models → agents in the workflow, Data silos.
Shares AlphaFold 2: predicting protein structures to near-experimental accuracy, Pathology & radiology foundation models, Digital pathology & AI.
Shares AI in the oncology clinic: from narrow cleared tools to multimodal decision support, Tempus AI, AI in oncology roadmap: pattern readers → foundation models → agents in the workflow.