The distance between care and discovery is too large. A patient signal becomes a file, the file becomes a dataset, the dataset becomes a research question, and the result often returns too late.
Sarovi's compute direction is built around a different loop: clinical signal to biological mechanism to simulation to candidate intervention to clinical review. Not every case needs this depth, but the infrastructure should exist when the question deserves it.
Discovery should learn from real patients
Drug discovery and translational research often operate far from the daily reality of care. Sarovi wants to bring the layers closer: imaging phenotypes, lab trajectories, molecular assays, patient embeddings, pathway hypotheses, protein structures, docking, and clinical response.
The long-term ambition is not only faster research. It is more personalized research: the ability to ask why this patient, with this biology, under this treatment, changed in this way.
References
- National Human Genome Research Institute, DNA Sequencing Costs Data, cost trends that make broader molecular data generation possible.
- NVIDIA, Accelerating Healthcare Innovation with AI, external video context for accelerated AI in healthcare.
- WHO, Ethics and governance of artificial intelligence for health, governance context for AI systems operating near care.
- European Commission, European Health Data Space, European framework for health data use in care and research.