Artificial Intelligence (AI) can be defined as the theory and development of computer systems capable of performing tasks that typically require human intelligence, including visual perception, speech recognition, decision-making, and language translation. The primary objective of AI development is to create machines or software that can simulate human activities and reasoning, such as image recognition, language comprehension, problem-solving, and decision-making through learning from errors. AI is rapidly advancing and offers significant opportunities for progress and applications across various healthcare fields. In nutrition research, AI's ability to extract, structure, and analyze extensive data from social media platforms is enhancing our understanding of dietary behaviours and perceptions. Additionally, AI-powered tools can aid in tracking dietary intake, providing feedback, and encouraging healthier food choices, though their adoption in clinical nutrition brings ethical and regulatory concerns, such as data privacy and potential bias. This article aims to review the current applications of AI in nutrition science research, exploring its growing impact on medical diagnostics, risk prediction, and therapeutic support

Artificial Intelligence (AI), Optimal diet, Gut microbiome, Mobile health (mHealth) applications, Step Tracker, Image analysis

In conclusion, the future of AI in nutrition holds great promise, presenting a myriad of opportunities for advancements that span personalized health solutions to global food security initiatives. AI's capability to analyze vast datasets, including genetic information, biomarkers, dietary habits, and health records, allows for the creation of highly personalized dietary recommendations tailored to individual needs such as age, gender, health conditions, and personal preferences. This personalized approach has the potential to revolutionize how we approach nutrition, shifting from generalized guidelines to targeted interventions that optimize health outcomes. Furthermore, AI can play a crucial role in enhancing food safety by predicting and detecting contaminants, spoilage, and pathogens in food supply chains. It can also improve food quality through advanced quality control mechanisms and ensure compliance with nutritional labeling standards. Beyond individual health and safety, AI-driven insights can inform public policy and interventions aimed at addressing global nutrition challenges like food insecurity and malnutrition. However, realizing the full potential of AI in nutrition requires continued research and development to refine algorithms, improve accuracy, and expand the scope of applications. Ethical considerations surrounding data privacy, consent, and algorithmic bias must also be addressed to ensure responsible deployment and equitable access to AI-driven nutrition solutions worldwide

Midhila Mahendran, Karthika B., Manoj Kumar Verma and Krishnaja U. (2024). AI Innovations in Nutrition: A Critical Analysis. Biological Forum – An International Journal, 16(9): 124-132