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VOL. 8, ISSUE 1 (2026)
Artificial intelligence in predicting PCOD risk and designing preventive nutrition therapies: A review
Authors
Supriti Mandal, Souvik Tewari, Shweta Parida, Shilpa Saha
Abstract
Polycystic Ovary Disease/Polycystic Ovary
Syndrome (PCOD/PCOS) is one of the most common endocrine and metabolic
disorders affecting women of reproductive age, characterized by hormonal
imbalance, insulin resistance, menstrual irregularities, and increased risk of
long-term metabolic and psychological complications. Early identification and
personalized preventive strategies are crucial for effective management of
PCOD. In recent years, Artificial Intelligence (AI) has emerged as a powerful
tool for predicting disease risk, analyzing complex biomedical data, and
supporting precision nutrition approaches. This review explores the application
of AI and machine learning techniques in predicting PCOD risk and designing
personalized preventive nutrition therapies. It discusses data sources,
predictive models, nutritional decision-support systems, clinical implications,
challenges, and future perspectives, highlighting AI’s potential to transform
PCOD prevention and management.
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Pages:1-5
How to cite this article:
Supriti Mandal, Souvik Tewari, Shweta Parida, Shilpa Saha "Artificial intelligence in predicting PCOD risk and designing preventive nutrition therapies: A review". International Journal of Gynaecology and Obstetrics Research, Vol 8, Issue 1, 2026, Pages 1-5
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