Artificial intelligence is revolutionizing medical diagnosis through advanced pattern recognition and predictive analytics. This study evaluates the performance of deep learning algorithms in diagnosing skin cancer, diabetic retinopathy, and pneumonia from medical imaging. We trained convolutional neural networks on datasets containing 125,000 medical images and achieved diagnostic accuracies of 94.2% for skin cancer, 91.8% for diabetic retinopathy, and 89.5% for pneumonia. While AI demonstrates superior performance in pattern recognition, our analysis reveals critical challenges including dataset bias, explainability issues, and regulatory requirements. The research provides guidelines for responsible AI implementation in healthcare settings.