Health
AI-driven program offers personalized recommendations to reverse chronic metabolic diseases such as diabetes
A groundbreaking artificial intelligence-powered diabetes management program, TWIN Health’s Whole Body Digital Twin, has garnered praise from experts for its innovative approach to empowering individuals in reversing chronic metabolic diseases, especially during religious fasts. The technology utilizes AI and machine learning to create a personalized “digital twin” of an individual’s unique metabolism, providing precise nutrition guidance and tailored meal plans for optimal blood sugar control during fasts.
Endorsed by Dr. Mohsin Wali, a Senior Consultant at Sir Ganga Ram Hospital in Delhi, the Whole Body Digital Twin is hailed as revolutionary for its potential in managing diabetes during religious fasts. Dr. Wali emphasized the personalized approach and data-driven insights offered by TWIN Health, stating that it has the power to transform the lives of individuals with diabetes and enable them to make informed health decisions.
Dr. Shashank Joshi, Chief Scientist at TWIN Health and a Mumbai-based endocrinologist, highlighted the research and clinical outcomes supporting the technology’s ability to facilitate diabetes remission and assist individuals in safely reducing or eliminating medication usage, particularly during Ramadan. Dr. Joshi underscored the profound impact on overall health and wellbeing, noting that personalized sleep recommendations, adjusted exercise routines, and stress management techniques further enhance metabolic health.
Individuals can access TWIN Health by creating an account on the website, completing an initial health assessment, and utilizing the platform’s tools and features to enhance their overall health and lifestyle. The technology’s scientific validation and personalized guidance in nutrition, sleep, activity, and breathing make it a promising tool for managing and reversing chronic metabolic diseases, offering individuals a valuable resource for improving their well-being.
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