This technology integrates AI-PRS (Artificial Intelligence-guided Phenotypic Response Surface) with zebrafish liver cancer models to establish a visualized and quantifiable decision-making platform for integrative therapies. It rapidly identifies the optimal combinations of natural compounds and targeted drugs, achieving enhanced efficacy with minimized toxicity. The platform demonstrates strong translational potential, offering a novel tool for personalized therapy design and exhibition-ready applications.

By analyzing dose-response curves of cancer versus normal cells, the system identifies a “golden dosage window” that maximizes efficacy while minimizing toxicity. The approach has been validated in multiple natural compound and targeted drug combinations, with demonstrated in vivo anti-cancer effects in zebrafish models. It provides a robust translational platform with exhibition-ready live imaging, drug response landscapes, and AI-based analytics.

Integrating AI computation with zebrafish in vivo modeling and an East-West integrative medical perspective, the platform rapidly evaluates the efficacy and toxicity of drug combinations in physiological contexts. Compared with conventional cell-based assays, it offers superior accuracy and efficiency. Potential applications include natural product drug development, personalized dosing optimization, and AI-powered clinical decision support systems, with significant commercialization and international collaboration prospects.