Key Takeaways

  • A principal investigator at Sanford Burnham Prebys received $100,000 in funding and $100,000 in AWS promotional credits to develop Leitstern
  • The AI system is intended to accelerate pediatric-cancer data analysis and personalized treatment selection
  • The award reflects growing demand for cloud infrastructure across genomics, digital biology, and clinical decision support

A principal investigator at Sanford Burnham Prebys has received the 2026 AWS Children’s Health Innovation Award to develop Leitstern, an artificial intelligence system designed to help clinicians analyze pediatric-cancer data and identify personalized treatment options more quickly.

The award combines $100,000 in funding with $100,000 in AWS promotional credits, giving the project both financial support and access to cloud computing resources. A Bioengineer report described the package as a $200,000 cloud award focused on accelerating personalized cancer care for children.

For pediatric oncology, speed has unusual importance. Childhood cancers can involve rare molecular profiles, relatively small patient populations, and limited clinical evidence for any one subtype. Clinicians may need to consider genomic findings, medical records, published research, and potential therapies within a compressed decision window. Leitstern is intended to make that information easier to analyze, although clinical validation and physician oversight will remain central to its use.

Generating biological data is no longer the only bottleneck; interpreting it to support individual treatment decisions presents equal difficulty.

Cloud infrastructure directly addresses these intensive compute requirements. Genomic and multi-omics analysis frequently demands bursts of high-performance computing rather than a fixed amount of capacity. Promotional credits enable research teams to run larger experiments, test models, and store extensive datasets without the upfront infrastructure investment required for on-premises environments.

The award aligns with the broader work of the Sanford Burnham Prebys Center for Data Science and Artificial Intelligence, which applies computational approaches to biomedical discovery. Leitstern applies this research mission directly to clinical workflows, aiming to translate complex pediatric-cancer data into actionable information clinicians can assess within existing care processes.

Cloud adoption across life sciences provides some commercial context. According to Mordor Intelligence, cloud deployment represented 51.44% of the life-science software market in 2025 and is forecast to expand at a 12.77% compound annual growth rate through 2031. Separately, the global digital-biology market is projected to grow from $17.35 billion in 2026 to $46.1 billion by 2033, a 15% compound annual growth rate. Cloud deployment is projected to account for 63.6% of that market in 2026.

Those numbers point to a wider infrastructure shift. AWS, Google Cloud, and Microsoft Azure provide scalable compute, storage, and machine-learning services used in life-sciences research. Cloud-based high-performance computing captured 59% of new HPC demand in 2024, reflecting its role in data-intensive AI and research workloads.

Still, compute capacity alone does not produce a dependable clinical system. Pediatric-cancer information can be fragmented across sequencing platforms, research databases, hospital systems, and unstructured documents. Data quality, lineage, access controls, model transparency, and interoperability can shape whether an AI recommendation is useful. Can a clinician see why a system surfaced one treatment option over another? That question may matter as much as raw model performance.

The FAIR Principles, which encourage research data to be findable, accessible, interoperable, and reusable, offer one guide for organizing scientific information. HL7 FHIR can support healthcare-data exchange. Neither removes the operational work involved, but both can help teams avoid building isolated pipelines that become difficult to maintain or connect with clinical systems.

There are business considerations too. The worldwide healthcare-cloud-computing market was estimated at $20.29 billion in 2025 and is forecast to reach approximately $69.4 billion by 2035, according to Precedence Research. Research programs such as Leitstern illustrate why spending is rising: advanced biomedical AI can require flexible infrastructure well before it becomes a production clinical application.

AWS has awarded more than $21 million in unrestricted funding and cloud support through the Imagine Grant program since its 2018 launch. Sanford Burnham Prebys researchers now have additional resources to test whether AI-assisted analysis can shorten the path from complex pediatric-cancer data to personalized treatment options that clinicians can evaluate for individual patients.