What was the impact of the AI solution?

Literature review time reduced from hours to minutes. The team is expanding the approach with partners for Neuro-Oncology and Neuroblastoma. Rutgers Health is expanding on the foundation to integrate with tumor boards and evaluate algorithm performance.

Rank#280of 3,449

Source

GCPEvidence: Low35/100

The Princess Máxima Center for Pediatric Oncology speeds up iLTB literature reviews using PubMed data in BigQuery + Gemini

Use case typeClinical decision supportUpdated Dec 4, 2025

The Princess Máxima Center for Pediatric Oncology in the Netherlands developed a system that combines PubMed data in BigQuery with Gemini models to revolutionize their international Leukemia Tumor Board (iLTB). By consolidating medical literature analysis into BigQuery, they can now provide comprehensive literature reviews in minutes rather than hours.

Organization: The Princess Máxima Center for Pediatric Oncology
Industry: Healthcare
Location: Netherlands
Published: December 2025

Reported outcomes

−90%

literature review timeTime & speed

Strategic outcomes

Speed & agilityAccelerated literature reviews for tumor board decisionsScale & capacityExtended the approach to additional oncology programs

Why do we believe this?Outcome claims, sources, and evidence checks

Normalized claim

Literature review time: 90% decrease

Google Cloud BlogDec 4, 2025Blog postInferred claimLow evidence strength

provide comprehensive literature reviews in minutes rather than hours

Why do we believe this deployment?Customer identity, provider attribution, maturity, and source checks

Customer: The Princess Máxima Center for Pediatric Oncology, Rutgers Health
Provider: GCP
Maturity: Unknown
Linked source: Google Cloud Blog

No explicit deployment-stage evidence found.

Customer identity supportedSource describes one deploymentMaturity evidence evaluated

Primary read

Use case focus

Showing 2 of 2

1Clinical decision support
2Semantic search

Tumor board teams struggled with manual, time-consuming international literature reviews and semantic mismatches in keyword search, slowing clinical decision making and drug repurposing research.

The organization built a system combining PubMed data in BigQuery with Gemini models and Vertex AI vector search capabilities to perform semantic search over biomedical literature and generate comprehensive literature reviews for the Leukemia Tumor Board.

Technologies

BigQuery Gemini Vertex AI

Literature review time reduced from hours to minutes.
The team is expanding the approach with partners for Neuro-Oncology and Neuroblastoma.
Rutgers Health is expanding on the foundation to integrate with tumor boards and evaluate algorithm performance.

Architecture

A system built on PubMed data in BigQuery with Gemini models and Vertex AI vector search capabilities to semantically search biomedical literature and generate literature reviews for the international Leukemia Tumor Board.

Sources & evidence1

Evidence: Low35/100Evidence strength