In most clinics, large volumes of oncology data are scattered across separate systems, which makes evidence-based improvements slow and fragile. By directly connecting to primary oncology data sources, RayIntelligence helps institutions learn from their own data and adapt their practice, assets, and skills on a continual basis.
With the new customization tools, clinics can create dashboards and charts tailored to their operations and priorities. These views are fully configurable, with examples ranging from plan quality trends and machine utilization to protocol adherence and outcome signals.
RayIntelligence v2025 provides a single, comprehensible intelligence layer that equips clinical teams to run a self-improving oncology program, enabling clinics’ readiness today and tomorrow.
Radiation oncology must operate as a self-improving, live program that learns from its own data and continuously adapts. Without it, clinics’ relevance, safety, accreditation, and long-term economics are at risk.
Implementing institutional improvements requires clarity, not more data. Yet the evidence needed to improve is spread across disconnected systems, making adaptation slow and too often guesswork.
RayIntelligence cuts through the noise of fragmented oncology data to give clinical teams a clear, improvement-driving signal, so decisions are timely, traceable, and repeatable.
Slide through example dashboards that turn oncology data into clear signals across plan quality, machine utilization, protocol adherence, outcome signals, and more. All views are configurable.
RayIntelligence is a cloud-based platform built on Amazon Web Services (AWS). Capacity scales with demand, and data is backed up and protected in a secure AWS environment.
Access from any location supports collaboration across teams and advisors. Updates are automatic, eliminating local hardware management and server patching.
RayIntelligence provides built-in security: encryption in transit and at rest, SAML-based single sign-on, and continuous monitoring safeguard availability and integrity, keeping the clinical data signal ready when needed.
Real oncology needs often emerge not within a single clinic but at population scale, where data noise multiplies. To surface those needs, a standardized analytics platform is essential.
RayIntelligence is used in KAYAC+, an EU-funded consortium coordinated by TU Dresden/OncoRay to improve radiotherapy for adolescents and young adults across Europe. The project links radiotherapy data from leading centers and is building a core of a future European data warehouse for cross-center treatment outcomes.
Within KAYAC+, RayIntelligence serves as a standardized analytics system, harmonizing multicenter oncology datasets into a shared, population-level signal.
Recorded live at ESTRO 2025, this webinar explores which clinical data matter most in daily radiotherapy workflows, what is still missing, and how RayIntelligence can help turn data noise into a clear, decision-driving signal for a self-improving oncology program.
Speakers: Michele Zeverino, Medical Physicist, CHUV, and Fredrik Löfman, Director of Machine Learning, RaySearch Laboratories.
Radiation oncology must operate as a self-improving, live program that learns from its own data and continuously adapts. Without it, clinics’ relevance, safety, accreditation, and long-term economics are at risk.
Implementing institutional improvements requires clarity, not more data. Yet the evidence needed to improve is spread across disconnected systems, making adaptation slow and too often guesswork.
RayIntelligence cuts through the noise of fragmented oncology data to give clinical teams a clear, improvement-driving signal, so decisions are timely, traceable, and repeatable.
