Leveraging our database of over one million categorised cancer patient records, we are developing machine learning algorithms capable of identifying individuals at high risk for cancer—based solely on structured, self-reported symptoms and personal health data, without the need for radio-diagnostics at the first stage. In patient side, our AI-guided questionnaire dynamically adjusts to patient inputs, producing a clinically validated risk score and recommending next steps. This system empowers earlier recognition of subtle cancer signals—particularly valuable for underdiagnosed or fast-progressing cancers—and supports earlier referral from primary care, even in settings with limited access to diagnostics. In physician side, our platform would incorporate a data-driven risk stratification module that supports GPs in identifying high-risk patients based on symptom patterns, history, and risk factors. This tool can flag patients who may benefit from expedited diagnostic referral—even before classic red-flag symptoms emerge—thus enabling earlier intervention for cancers often diagnosed too late (e.g., lung, colorectal, pancreatic).