{"entity":{"id":"idea-data-ai-vs-tumour-board-rct","kind":"idea","name":"A randomised trial of AI-generated treatment recommendations versus tumour boards","aka":[],"tldr":"Test head to head whether an AI that reads the record and the evidence recommends treatments as well as a panel of experts, and whether patients do as well.","summary":"AI systems that propose treatment plans from the record and the literature have been compared with tumour boards only retrospectively, with concordance as the metric. The proposal is a prospective, randomised non-inferiority trial in a defined setting (for example, first-line metastatic NSCLC or colorectal cancer): patients are randomised to have their plan generated by the AI (with clinician sign-off and override) or by the standard board, with guideline concordance, time to treatment, trial enrolment and 12-month outcomes as endpoints, and full provenance logging for every AI recommendation.","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-data-tumour-board-evidence-assistant","idea-data-provenance-first-decision-support"],"cancers":[],"sections":["ai-computation"],"technologies":[],"targets":[],"drugs":[],"companies":[],"institutions":[],"pathways":[],"terms":[],"trials":[],"people":[],"bottlenecks":["b-ai-validation","b-workforce","b-knowledge-diffusion"],"keyPapers":["paper-wu-nat-med"],"journals":[],"dependsOn":[],"notes":[],"hypothesis":"AI-generated plans with clinician sign-off will be non-inferior to tumour boards on guideline concordance and clinical outcomes and superior on time to treatment and trial referral, in the studied settings.","rationale":"Tumour board capacity is a bottleneck and boards show variable concordance; if AI can match them in defined settings, expert time can be redirected to complex cases. The claim is testable only prospectively.","test":"A 600-patient randomised non-inferiority trial in one indication across five centres; pre-registered with concordance, time to treatment and 12-month PFS endpoints.","maturity":"speculative","actor":"research","cost":"medium","horizonYears":3},"route":"/ideas/idea-data-ai-vs-tumour-board-rct/","neighbours":{"idea":[{"id":"idea-data-tumour-board-evidence-assistant","kind":"idea","name":"A tumour board assistant that cites its evidence and tracks outcomes","route":"/ideas/idea-data-tumour-board-evidence-assistant/"},{"id":"idea-data-provenance-first-decision-support","kind":"idea","name":"Decision support that cites the exact trial and guideline line it relies on","route":"/ideas/idea-data-provenance-first-decision-support/"}],"section":[{"id":"ai-computation","kind":"section","name":"AI & Computation","route":"/fronts/ai-computation/"}],"bottleneck":[{"id":"b-ai-validation","kind":"bottleneck","name":"AI that is built but not validated or deployed","route":"/bottlenecks/b-ai-validation/"},{"id":"b-knowledge-diffusion","kind":"bottleneck","name":"Knowledge reaches practice too slowly","route":"/bottlenecks/b-knowledge-diffusion/"},{"id":"b-workforce","kind":"bottleneck","name":"Not enough oncologists, nurses, pathologists, physicists","route":"/bottlenecks/b-workforce/"}],"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/"}]}}