Multiagent GenAI-Powered Oncology Research with GraphMedAI and Neo4j

Hanafi will introduce GraphMedAI, an AI-driven platform revolutionizing oncology care and clinical research by bridging critical gaps between patients, healthcare professionals, scientific experts, and pharmaceutical companies. Attendees will learn how multiagentic AI dynamically constructs, queries, and enriches a Neo4j knowledge graph to deliver real-time precision insights for:

1. Expert collaboration with centers of excellence
2. Data-driven decision-making for researchers and pharma teams

Key technologies demonstrated include

• Multiagent GenAI: Orchestrates intelligent workflows for trial feasibility analysis and expert-institution matching
• RAG: Integrates live data from PubMed, ClinicalTrials.gov, and partner institutions
• Neo4j graph database: Maps and traverses complex medical relationships (e.g., patient-disease-trial-expert networks)

Attendees will gain

• Technical insights: How to structure healthcare data in Neo4j for high-performance search and recommendations
• Validation frameworks: Combining open datasets (e.g., PubMed) with clinical expertise to audit AI outputs
• Implementation patterns: Architecting scalable, graph-powered AI solutions for medical research

Designed for healthcare technologists, data engineers, and Neo4j developers, this session will showcase real-world use cases and provide actionable strategies to:

• Reduce trial recruitment timelines using graph-powered patient matching
• Accelerate bibliometric research through AI-augmented knowledge graphs
• Foster partnerships between academia, clinics, and pharma

Speakers: Hanafi Yakouben & Zouhair Allaoui