Blending Medical Insights with Scalable AI for Predictive Healthcare and Mental Health Solutions

Abstract

Bringing together artificial intelligence and clinical expertise is starting to reshape how we approach both physical and mental healthcare. With growing pressure on health systems to deliver more personalized and proactive care at scale, we set out to understand how AI can be used more meaningfully, not as a black-box solution, but as something grounded in real clinical insight. In this work, we explore a framework that combines semi-supervised learning, deep convolutional neural networks, and ensemble methods to make sense of complex health data. That includes inputs like wearable sensor readings, electronic health records, and genomic profiles. On the mental health side, we trained emotion prediction models using longitudinal behavioral data to help flag early signs of depression and anxiety. For physical health, our models performed well on conditions like skin cancer and diabetes, with an AUC of 0.94 and an F1 score of 0.91 on our test sets. Performance metrics aside, we put a lot of weight on making these models understandable and clinically relevant. We used SHAP values to explain which features were driving predictions and wove in domain expertise throughout the process, from how we prepared the data to how we assessed the models. The goal wasn’t just to make something that worked, but something that could actually inform care decisions. We also looked at the infrastructure side of things. For AI to be deployed safely and at scale, especially across populations, you need more than good algorithms. Our study points to the importance of cloud infrastructure, spatial data tools, and federated learning in supporting this kind of deployment