This paper presents a comparative recognition approach to artificial intelligence (AI) using a binary comparison format (BCF) to recognize and classify images effectively. Unlike traditional artificial neural networks (ANNs), which rely on gradient descent learning and require more computing power, energy, and human labor, the comparative approach uses structured comparison libraries for similarity-based recognition. This reduces computational complexity and radically reduces recognition time, ensuring transparency of processes and interpretability of AI results. Both digital and analog implementations for various applications, including medical diagnostics, are briefly described. A software product illustrates the work carried out, PANC_Platform program, providing recognition and cross-classification of various kinds of images and transferring information from memory to the recognized image. The product is available for free download for testing and non-commercial use.