ne of my most memorable AI training experiences was working on a project that involved labeling thousands of medical X‑r
ne of my most memorable AI training experiences was working on a project that involved labeling thousands of medical X‑ray images. The goal was to train a model to detect early signs of pneumonia. Each image had to be carefully annotated—identifying lung regions, marking abnormalities, and distinguishing between healthy and diseased tissue. Because accuracy was critical, the labeling process required collaboration with medical professionals to ensure the tags reflected real diagnostic criteria. The labeled dataset was then used to train a convolutional neural network (CNN). During training, the model gradually learned to recognize subtle differences in lung patterns. Watching the accuracy improve with each iteration was rewarding, and it highlighted how essential precise data labeling is for building trustworthy AI systems