{"entity":{"id":"idea-data-digital-twin-predict-then-observe","kind":"idea","name":"Digital twins for treatment selection, validated by predicting before observing","aka":[],"tldr":"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.","summary":"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.","asOf":"2026-09-08","links":[{"label":"Bottleneck evidence (AI that is built but not validated or deployed): Wu et al., How medical AI devices are evaluated: limitations and recommendations from an analysis of FDA approvals (Nature Medicine 2021)","url":"https://doi.org/10.1038/s41591-021-01312-x"}],"tags":[],"related":["idea-multimodal-foundation-model"],"cancers":[],"sections":["ai-computation"],"technologies":["organoids","functional-drug-testing"],"targets":[],"drugs":[],"companies":[],"institutions":[],"pathways":[],"terms":[],"trials":[],"people":[],"bottlenecks":["b-ai-validation","b-tumor-heterogeneity"],"keyPapers":["paper-wu-nat-med"],"journals":[],"dependsOn":[],"notes":[],"hypothesis":"Prospectively locked twin predictions will achieve clinically useful calibration for at least one common decision (for example, response to first-line chemo-immunotherapy in NSCLC), and a randomised trial of twin-informed selection will improve response rates.","rationale":"Weather and engineering models earned trust through routine, scored forward prediction; medical models have skipped this step and are trusted or dismissed on retrospective fits.","test":"Enrol 500 patients across two cancers; lock predictions; publish calibration plots and Brier scores; proceed to a randomised trial only if pre-specified thresholds are met.","maturity":"preclinical-evidence","actor":"research","cost":"medium","horizonYears":4},"route":"/ideas/idea-data-digital-twin-predict-then-observe/","neighbours":{"idea":[{"id":"idea-data-in-silico-trials-calibrated","kind":"idea","name":"In silico trials to prioritise combinations, scored against later real trials","route":"/ideas/idea-data-in-silico-trials-calibrated/"},{"id":"idea-multimodal-foundation-model","kind":"idea","name":"Patient-level multimodal foundation models for treatment selection","route":"/ideas/idea-multimodal-foundation-model/"}],"section":[{"id":"ai-computation","kind":"section","name":"AI & Computation","route":"/fronts/ai-computation/"}],"technology":[{"id":"functional-drug-testing","kind":"technology","name":"Functional (ex vivo) drug testing","route":"/technologies/functional-drug-testing/"},{"id":"organoids","kind":"technology","name":"Patient-derived organoids","route":"/technologies/organoids/"}],"bottleneck":[{"id":"b-ai-validation","kind":"bottleneck","name":"AI that is built but not validated or deployed","route":"/bottlenecks/b-ai-validation/"},{"id":"b-tumor-heterogeneity","kind":"bottleneck","name":"Tumour heterogeneity and clonal evolution","route":"/bottlenecks/b-tumor-heterogeneity/"}],"paper":[{"id":"paper-wu-nat-med","kind":"paper","name":"How medical AI devices are evaluated: limitations and recommendations from an analysis of FDA approvals","route":"/key-papers/paper-wu-nat-med/"}]}}