AI learned language, then vision, then the rules of proteins. The next frontier is disease over time, not as static snapshots, but as living systems that evolve. Cancer is the proving ground, and drug resistance is where those trajectories are most learnable.
Standard of care has shifted from chemo to a deep arsenal of targeted drugs, each with its own resistance pathways, and nearly all eventually stop working.
Liquid biopsy has crossed clinical validation, detecting progression a median of roughly 151 days before imaging in the literature.
Therapies built to overcome resistance already exist. The missing piece is finding the right patient before the window closes.
Oncology decides care from snapshots of a moving disease. If we can learn the trajectory itself, we can act before the window closes, for every patient, not just the ones scanned at the right moment.
We're building a longitudinal understanding of how each patient's disease evolves, learned one trajectory at a time. Our ambition is the world's most accurate models of disease over time for precision oncology, so every patient receives the right therapy at the right time.
The more patients Span helps, the sharper its predictions become. That compounding is the moat.