The review describes an innovation cycle much broader than using a chatbot. AI is being applied to finding new sources, identifying compounds, interpreting nutritional mechanisms, personalisation and process control.

In scouting, models can rank large candidate spaces and connect structures, omics data and expected properties. In development, they can compare formulation and process variables, reducing the number of combinations sent to the laboratory.

The value for R&D emerges when every prediction retains a trail to data, samples, analytical methods and experimental conditions. Without that infrastructure, a model mainly accelerates the production of hypotheses that are difficult to verify.

The review does not establish efficacy for any ingredient or make an output automatically manufacturable. AI value remains conditional on validation, reproducibility, human control, safety, production feasibility and regulation.

Why it matters

For the nutraceutical industry, the review helps distinguish operational applications from promises that still require biological and industrial validation.

Evidence limit

This is a literature review, not validation of an individual model or ingredient. Computational performance does not replace experimental confirmation, scale-up, safety or regulatory assessment.

Editorial note. This content is intended for industry professionals. It does not constitute medical advice, therapeutic guidance or regulatory advice. Read our editorial method.

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