Precision to plate: AI-driven… | 2025

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43/100

✓ Peer-reviewed alzhub usefulness score 43 out of 100. Prevention and risk

Topic: Risk, prevention and care in population studies

The global food system faces unprecedented challenges, including climate-driven agricultural instability, rising malnutrition, and consumer demand for sustainable yet appealing products. Artificial intelligence (AI) has emerged as a transformative force in addressing these challenges, enabling breakthroughs from microbial engineering to individualized dietary solutions. This review synthesizes advances in AI-driven precision fermentation-where CRISPR-based microbial optimization and reinforcement learning accelerate bioactive compound synthesis-and hyper-personalized nutrition, where predictive modeling tailors diets to genetic, metabolic, and cultural profiles. We highlight how AI decodes sensory attributes (e.g., flavor and texture) through deep learning and natural language processing, bridging gaps between lab-scale innovation and consumer acceptance. However, the adoption of these technologies raises critical ethical concerns, including data privacy risks from wearable health monitors and algorithmic biases exacerbating nutritional disparities. Key findings include 300% yield increases for alt-proteins via AI-CRISPR fusion, 60% reduction in bioreactor failures through RL optimization, and 25% lower childhood anemia rates via equitable AI-nutrition platforms-though ethical gaps persist in data privacy (72% GDPR non-compliance) and algorithmic bias. By analyzing regulatory frameworks and proposing equity-focused design principles, this article advocates for a balanced approach to AI deployment in food systems. Emphasis on digital transformation, we underscore AI's potential to democratize sustainable food production while urging collaborative governance to ensure transparency and inclusivity. This work serves as a roadmap for researchers, policymakers, and industry stakeholders navigating the intersection of AI, biotechnology, and nutrition science.

Key findings

However, the adoption of these technologies raises critical ethical concerns, including data privacy risks from wearable health monitors and algorithmic biases exacerbating nutritional disparities.
Key findings include 300% yield increases for alt-proteins via AI-CRISPR fusion, 60% reduction in bioreactor failures through RL optimization, and 25% lower childhood anemia rates via equitable AI-nutrition platforms-though ethical gaps persist in data privacy (72% GDPR non-compliance) and algorithmic bias.
By analyzing regulatory frameworks and proposing equity-focused design principles, this article advocates for a balanced approach to AI deployment in food systems.
Emphasis on digital transformation, we underscore AI's potential to democratize sustainable food production while urging collaborative governance to ensure transparency and inclusivity.
This work serves as a roadmap for researchers, policymakers, and industry stakeholders navigating the intersection of AI, biotechnology, and nutrition science.

Why it matters

This review is about artificial intelligence in fermentation and personalised diets, and does not address Alzheimer's disease treatment, cure or prevention. It reports findings on food production yields, bioreactor failures and childhood anaemia, along with ethical concerns about data privacy and algorithmic bias.

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Study details

Evidence strengthLow

How this study scored 43/100

Published in a peer-reviewed journal. Scored zero for study design, sample size, effect reported, studied in people and randomised / blinded because the record does not state it.

Component	Points	Why
Journal tier	7 / 10	Tier 3 — peer-reviewed journal (Frontiers in Nutrition)
Journal's citation record	1 / 4	Frontiers in Nutrition: journal h-index 147 (OpenAlex, 2026-10-29)
Study design	0 / 12	No study design recorded — scores zero, not guessed (not stated in the record — scored 0)
Strength of evidence	1 / 5	Rated “low” in our summary
Peer-review validation	10 / 10	Peer-reviewed. Published as an article in a named peer-reviewed journal.
How recent	4 / 5	2025 — within 3 year(s) of the newest study here
Sample size	0 / 4	Participant count not stated — scores zero (not stated in the record — scored 0)
Effect reported	0 / 3	No effect size reported — scores zero (not stated in the record — scored 0)
Studied in people	0 / 3	Not stated whether this was studied in people — scores zero (not stated in the record — scored 0)
Randomised / blinded	0 / 2	No randomisation or blinding stated — scores zero (not stated in the record — scored 0)
Times cited	4 / 7	Cited 51 times (OpenAlex, 2026-10-10)
Citations per year	3 / 5	17.0 citations a year over 3 year(s) since 2025
Impact for its field and year	5 / 6	Cited 28.1× the average for papers of its field and year (OpenAlex FWCI)
Cited by studies here	0 / 6	No other study in this library cites it
Still being cited	3 / 4	Cited 51 times in 2024–2026 (2026 still in progress; OpenAlex)
Senior author's track record	1 / 4	Last-listed author V. Jegadeeswari: h-index 5 (OpenAlex, whole career)
Institution's track record	0 / 3	Tamil Nadu Agricultural University: institution h-index 180 (OpenAlex)
Multi-centre collaboration	0 / 3	OpenAlex lists 1 institution(s) in 1 country — scores zero (affiliation lists can be incomplete)
Indexed in PubMed	2 / 2	Indexed in PubMed (PMID 40977977)
Free to read	2 / 2	Free to read
Total	43 / 100	—

What each of these 20 components measures, and what this score is not →

Read the original

Full text: free to read — OpenAlex lists this copy as free to read: the link above (checked 6 Feb 1020).

Cite this study APA, Vancouver, BibTeX · download .bib or .ris

Built from the record alzhub holds for this paper, not looked up. Volume, issue and page numbers are not held in the record. Check the citation against the original before you use it.

APA 7th

Priyadharshini, D., Muthuvel, I., Saraswathy, S., Kavitha, P., & Jegadeeswari, V. (2025). Precision to plate: AI-driven innovations in fermentation and hyper-personalized diets. Frontiers in Nutrition. https://doi.org/10.3389/fnut.2025.1659511

Vancouver

Priyadharshini D, Muthuvel I, Saraswathy S, Kavitha P, Jegadeeswari V. Precision to plate: AI-driven innovations in fermentation and hyper-personalized diets. Frontiers in Nutrition. 2025. doi:10.3389/fnut.2025.1659511. PMID: 40977977.

BibTeX

@article{priyadharshini2025-40977977,
  author  = {Priyadharshini, D. and Muthuvel, Iyyamperumal and Saraswathy, Sethurathinam and Kavitha, P.S. and Jegadeeswari, V.},
  title   = {{Precision to plate: AI-driven innovations in fermentation and hyper-personalized diets}},
  journal = {Frontiers in Nutrition},
  year    = {2025},
  doi     = {10.3389/fnut.2025.1659511},
  pmid    = {40977977},
  url     = {https://doi.org/10.3389/fnut.2025.1659511}
}

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