AI in Medicine: The Challenge of Trusting What We Don’t Fully Understand
In the early days of artificial intelligence, machine learning models were pretty straightforward. We could look at them and understand how they worked. But things have changed. Today, the most powerful AI systems, especially those using deep learning, are almost impossible for us to “see inside.” This has made them incredibly useful for tasks like analyzing medical data, including diagnosing heart conditions through ECG signals. These deep neural networks (DNNs) are impressively accurate but bring a significant challenge: they operate as “black boxes”—producing results without showing us how they got there.
Let’s dig a little deeper into why this is such a big deal, especially in the field of medicine.