CanRisk-DB: an artificial intelligence-driven comprehensive database of cancer risk factors

Cancer development is influenced by a complex interplay of diverse risk factors, but synthesizing this fragmented research remains challenging. To address this, we developed CanRisk-DB, an Artificial intelligence (AI)-driven database that systematically aggregates and standardizes published evidence on cancer-associated risk factors. Using a multi-stage AI pipeline based on the PICOS framework, we analyzed 435,975 publications from PubMed, Embase, and Cochrane (2000-2024), employing a Graph-based Retrieval-Augmented Generation framework to extract cancer types, risk factors, and quantitative estimates (e.g., relative risk RR, hazard ratio HR, standardized incidence ratio SIR). In the literature screening stage, the system demonstrated high accuracy and efficiency. From 9550 relevant articles, CanRisk-DB compiled 445,646 standardized records covering 76 risk factor groups and 42 cancer types across 80 countries over 50 years. Validated against benchmark datasets, this publicly accessible resource constitutes a comprehensive knowledge base of cancer risk factors, supporting etiological research, risk analyses, and the development of evidence-informed prevention strategies. The CanRisk-DB is available at http://www.canrisk-ai.com .