Domain knowledge, just evaluation, and robust data standards are required to advance AI in food science

Background

Artificial intelligence (AI) has shown transformative potential across many scientific fields, including food science. Applications span nutrition, safety, flavor, and sustainability. However, current AI implementations in food science often lack integration with domain expertise, face reproducibility challenges, and are hindered by fragmented datasets and limited benchmarking.

Scope and approach

This perspective outlines key challenges and proposes five strategic initiatives to guide the effective and responsible integration of AI in food science. These include embedding domain knowledge into models, establishing transparent and reproducible workflows, adopting benchmarking practices, promoting practical validation, and developing robust data standards and infrastructure.

Key findings and conclusions

To fully unlock AI's potential in food science, future research must prioritize domain-aware model development, open science practices, and practical validation. These efforts are critical to enabling reliable, generalizable, and impactful AI tools that address real-world challenges in the food systems.