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Build a computer model of each patient's cancer that forecasts how it will respond to each treatment option, and prove it by writing the forecast down before the real result is known.
Patient digital twins (mechanistic, statistical or hybrid models of a patient's tumour and physiology) are proposed for treatment selection, but validation is almost entirely retrospective. The proposal is a validation programme with a strict protocol: for each enrolled patient, the twin's prediction (response, progression time, toxicity) for the chosen treatment is locked before treatment; predictions are compared with observed outcomes; calibration and discrimination are published. Only twins that pass proceed to trials where predictions inform choices.
Shares Patient-level multimodal foundation models for treatment selection, How medical AI devices are evaluated: limitations and recommendations from an analysis of FDA approvals, AI that is built but not validated or deployed.
Shares How medical AI devices are evaluated: limitations and recommendations from an analysis of FDA approvals, AI that is built but not validated or deployed.
Shares Functional (ex vivo) drug testing, Patient-derived organoids.
Shares Functional (ex vivo) drug testing, Patient-derived organoids.
Shares Functional (ex vivo) drug testing, Patient-derived organoids.
Shares Functional (ex vivo) drug testing, Patient-derived organoids.
Shares How medical AI devices are evaluated: limitations and recommendations from an analysis of FDA approvals, AI that is built but not validated or deployed.
Shares How medical AI devices are evaluated: limitations and recommendations from an analysis of FDA approvals, AI that is built but not validated or deployed.