🧠 I Stopped Thinking in Machine Learning Models and Started Thinking in Systems (Here’s Why) And it completely changed how I build AI for healthcare. Most machine learning projects look impressive in isolation. You train a model, get good metrics, maybe even build a notebook demo—and it feels like progress. But when you try to turn that into something real, especially in healthcare, something breaks. That’s what pushed me to rethink everything. Instead of focusing on models, I started focusing on systems. 🏥 The problem I started exploring In healthcare, a lot of valuable insights already exist in routine lab data—like blood counts and biochemical markers. The challenge is not data availability. It’s interpretation at scale. So I explored a question: What would it take to build an AI system that can process routine clinical data and generate meaningful early risk signals across multiple conditions? Not for one disease. But for multiple, in a unified system. Here's what I discovered along the way.…