This thesis explores the application of artificial intelligence methods in digital health, clearly demonstrating the pathway from technological breakthroughs to practical health solutions in four critical areas: plant disease detection, food recognition, personalized glycemic response forecasting, and musculoskeletal modeling. Leveraging deep learning and reinforcement learning techniques, this work highlights the feasibility of practical, scalable AI-driven solutions to longstanding healthcare challenges.

First, a deep convolutional neural network trained on an extensive dataset successfully identifies plant diseases from leaf images, presenting an effective approach to smartphone-assisted agricultural diagnostics. Next, advanced instance segmentation models enable accurate food segmentation and recognition from real-world images, facilitating improved dietary assessments essential for nutritional epidemiology. Subsequently, Temporal-Fusion-Transformers accurately forecast individualized postprandial glycemic responses using continuous glucose monitoring data and nutritional information, highlighting personalized nutrition possibilities. Finally, deep reinforcement learning methods are utilized to synthesize physiologically accurate human movements within musculoskeletal simulations, demonstrating potential applications in biomechanics and rehabilitation.

The thesis further emphasizes the transformative potential of crowdsourced participatory research to accelerate AI innovations, reducing barriers to practical implementation and promoting rapid, tangible public health impacts.