Use of artificial intelligence for assessing chemistry and safety of edible insects: A roadmap for functional innovation

Background: Edible insects are increasingly recognized as sustainable and nutritious alternatives to conventional animal proteins, yet their wider adoption is limited by compositional variability, food safety concerns, authentication challenges, and consumer trust. Addressing these issues requires integrative analytical approaches capable of handling complex biochemical and environmental data. Scope and approach: This commentary examines how artificial intelligence (AI) can support edible insect research across nutritional profiling, techno-functional modeling, bioactive compound discovery, safety assessment, and traceability. Rather than reviewing individual algorithms, we propose a conceptual roadmap that integrates multi-omic data, spectroscopic fingerprints, and environmental metadata into predictive and decision-support frameworks aligned with regulatory and governance needs. Key conclusions: AI enables a shift from descriptive analyses toward predictive and scalable models of insect composition, functionality, and safety. By linking molecular characterization with process- and system-level monitoring, AI-based frameworks can support dynamic hazard analysis, species authentication, and innovation pathways for insect-based foods. The novelty of this commentary lies in positioning AI as a unifying, cross-scale enabler that connects chemistry, safety, functionality, and governance within a coherent roadmap for future edible insect systems.