Curait Medical provides an AI-driven quality assurance platform for radiation oncology, automating the review of cancer radiation therapy prescriptions to detect errors that manual processes often miss. By integrating large‑language models and GPU acceleration, the solution increases accuracy, speeds up QA workflows, and reduces treatment costs, helping hospitals deliver safer, more efficient radiation therapy.
Funding
Funding not disclosed
Founders
Product
Problem
Radiation oncology departments rely on manual, subjective quality assurance (QA) for each cancer radiation therapy plan, a process that is time‑consuming, costly, and prone to missing conceptual prescription errors.
Solution
Curait Medical offers an AI‑QA platform that automatically reviews radiation therapy plans using large language models and GPU‑accelerated inference. The system analyzes prescription parameters, dose distributions, and treatment intent to identify errors that traditional manual checks often overlook. By delivering consistent, data‑driven assessments, the platform reduces the workload of physicists and clinicians while increasing detection accuracy. Faster QA cycles enable hospitals to treat more patients without compromising safety, ultimately improving clinical outcomes and lowering operational expenses.
Target Audience
Primary customers are radiation oncology departments in hospitals and cancer centers that perform large volumes of radiation therapy treatments and seek to modernize their QA workflows.
Features
- Deep‑learning models trained on millions of historical RT plans to flag prescription inconsistencies and dose‑calculation anomalies
- Real‑time integration with treatment planning systems for seamless, automated QA without manual data entry
- Explainable AI reports that highlight specific error sources and suggest corrective actions for clinicians
- Scalable GPU‑based inference engine delivering sub‑minute review times for each treatment plan
- Continuous learning pipeline that updates models with new clinical data to maintain accuracy across evolving protocols