Stanford postdoctoral researcher Ying Kejun, with scholars from the University of Washington, Harvard Medical School and MIT, posted HealthFlux on the medRxiv preprint server, describing it as the first pan-modal human health world model, trained on the UK Biobank's long-term cohort of roughly 500,000 people. The model maintains a health state described by 384 values that evolves on its own when no new data arrive and is corrected immediately when new measurements come in, advanced by neural ODEs, with 11 data types — records, blood tests, about 3,000 plasma proteins, some 250 metabolites and abdominal and brain MRI — feeding the same state; a separate reader extracts disease risk afterwards. The team reports a five-year average AUROC of 0.832 across 1,010 diseases and death in about 60,000 held-out participants, versus 0.805 for the prior multimodal model RisQ, and says the score held at 0.837 when data was cut off for 20 years while Delphi-2M fell to 0.576. The authors note the cohort is largely white and lacks psychological scales, and that the health state index has no warning threshold, so causal evidence for interventions and prospective validation are still missing.

HealthFlux·Stanford University·University of Washington·+5