In 2024, I completed Artificial Intelligence in Healthcare: Fundamentals and Applications through MIT xPRO. 

I now understand how AI makes decisions and the importance of training the model on a diverse data set. I’ve studied how AI systems can go astray, including “black box” logic, generative models that stop exploring new ideas, and feedback loops that reinforce bias instead of innovation.

Knowing these limitations actually made me more accepting of AI, because as smart as AI is these days, it’s still a long way behind humans when it comes to the work that really matters. Only humans can synthesize two disparate concepts into a novel insight. Only humans can weigh ethics against goals. And when given a list of options, only humans can reject them all and innovate.

When we’re willing to hand off some of the drudgery to AI--like make an outline, a schedule, or an edit--we free up time to spend on the human work that brings real value.

This foundation gives me strategic foresight about how AI can partner with humans in healthcare and design and shapes how I use these tools day-to-day. It also shapes how I evaluate emerging tools , weighing efficiency gains against ethical and human-centered considerations.