Nutrition, Diabetes and AI-driven Simulation of Mind Genomics Results: A New Way to Accelerate the Learning of ‘Softer Skills’ in Medicine
Dipak Paul, Howard Moskowitz, Henry Bernstein, David Stevens, Sharon Wingert.
This backgrounder brings together two complementary systems, AI for rapid scientific synthesis and Mind Genomics for mapping everyday thinking, to create a practical learning tool for understanding nutrition, obesity, and diabetes. Scientific evidence shows that global obesity and diabetes continue to rise because of powerful environmental, dietary, and behavioral forces, whereas behavioral research shows that people interpret health information through distinct mind sets that shape their decisions The AI/Mind Genomics Training Backgrounder integrates these two streams by summarizing validated scientific findings and identifying the specific messages that motivate different groups of people. The method uses structured experimental designs, individual level regression, and clustering to reveal mind sets that respond differently to risk based, convenience based, or family centered messages. The resulting system helps students, professionals, and the public learn quickly, accurately, and personally, moving from facts to understanding and from understanding to action. This approach supports tailored communication, more effective interventions, and a deeper appreciation of how scientific evidence and human decision making interact. The backgrounder therefore offers a new model for health education that respects both scientific rigor and human diversity.
