Abstract
Cancer stem cells (CSCs) exhibit self-renewal and multidirectional differentiation capacities. The stemness of CSCs is the fundamental cause of tumor progression and treatment resistance. The stemness index, evaluating the number and activity of CSCs, is a crucial indicator predicting various aspects of tumor behavior such as growth, metastasis, and prognosis. With the advancements in artificial intelligence (AI), particularly in data analysis and machine learning, the identification and understanding of CSCs' stemness characteristics have improved. The AI-based analysis allows for processing vast datasets and recognizing patterns that assist in comprehending the role of CSCs in cancer development. The utilization of AI to analyze and compute the stemness index holds significant clinical relevance in tumor diagnosis and treatment. This approach provides more precise and personalized information, potentially influencing treatment strategies. Therefore, tailoring treatments specifically targeting CSCs is highly imperative and may enhance therapeutic efficacy and outcomes in cancer patients.