Pathological diagnosis is the gold standard for confirming breast cancer. Traditional manual pathological diagnosis methods for breast cancer are time-consuming, labor-intensive, highly subjective, and exhibit poor diagnostic consistency. In recent years, artificial intelligence (AI) technology has rapidly advanced and is progressively being applied clinically as a promising early diagnostic tool. However, many existing AI models lack interpretability, which limits the trustworthiness of their clinical application. Khater et al, by combining a high-precision machine learning model with an explainable AI model, achieved highly accurate breast tumor diagnosis and provided explanations for key biological and pathological features influencing the diagnostic results. This points the way for the future application and development of AI in medical diagnosis and treatment. The article interprets the main content of that study, and analyzes the advantages and limitations of AI in medical diagnosis and treatment, with the aim of promoting its better application in clinical practice.
