How It Works
Step 1: Data Sourcing
We rely on extensive, anonymized datasets from trusted sources like the NHS, UK Biobank, and international databases. These datasets include:
Genomic Data: Insights into genes, metabolism, proteins, RNA, and gene expression.
Histopathological Data: High-resolution tissue samples analyzed for unique cancer patterns.
Clinical Data: Patient details including age, gender, lifestyle, family history, and medical background.
Step 2: Developing Predictive Models
Our AI engineers and oncologists collaborate to create models that predict individual patient responses. Using machine learning and deep learning techniques, we analyze complex datasets, filling in gaps and uncovering patterns to forecast treatment effectiveness.
Step 3: User-Friendly Interface
Our simple-to-use software integrates with existing hospital systems and provides quick, actionable insights. At the click of a button, doctors can instantly see a personalized treatment match for each patient, with percentages indicating how well a treatment is likely to work.
Results above 80-90% suggest a high probability of success, while lower scores indicate alternative treatments may be more effective.
Watch this short video for more information
