A doctor I work with told me: "Your model caught a tumor that I missed on the first read."

That sentence haunts me — in the best possible way.

AI in healthcare isn't a futuristic concept. It's happening now. Cancer detection from medical images that rivals (and sometimes exceeds) radiologist accuracy. Drug discovery timelines compressed from years to weeks. Predictive monitoring that alerts nurses before a patient deteriorates.

But here's what tech people get wrong about healthcare AI: the hardest part isn't the model. It's the trust, the regulation, and the integration into actual clinical workflows.

You can build the most accurate diagnostic model in the world. If doctors don't trust it, or if it doesn't fit into their workflow, or if it's not HIPAA compliant — it collects dust.

If you want to do meaningful AI work, healthcare is where technology meets real impact. But bring humility with your PyTorch skills. You're entering a domain where mistakes have consequences beyond a bad user experience.