Three Open Challenges for Biomedical World Models, Per Marinka Zitnik
marinkazitnik · x · 2026-09-18
Harvard's Marinka Zitnik outlines three key challenges for biomedical world models, tied to the paper "World models for biomedicine" (Cell special issue on AI in biology):
- Biological states are often only partially observed.
- Destructive assays yield snapshots, not trajectories over time.
- Observational data cover many states without interventions, while interventional data cover few.
Closing the gaps requires new data linking state, intervention, and outcome: non-destructive assays that measure the same cells repeatedly, organoids and organ chips producing human-relevant interventional trajectories, self-driving labs, and learning health systems embedding randomization into care.
Her litmus test for a world model: it must represent a system state, take a specified action, predict the resulting state, and continue simulation from there — so foundation models alone aren't world models, nor are digital twins that merely mirror one system's current state.
Related event: Harvard Team Publishes Biomedical World Models Framework in Cell(8 posts)→
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