Background Breast cancer is one of the leading causes of cancer-related mortality worldwide, affecting millions each year. Timely detection and accurate diagnosis are essential for improving survival rates and optimizing patient outcomes. As one of the most common malignancies, breast cancer demonstrates significantly improved prognoses when identified in its early stages. This study introduces a novel machine learning model powered by artificial intelligence, developed to support healthcare professionals in the early and accurate diagnosis of breast cancer using medical imaging. By harnessing advanced computational methods, the model enhances diagnostic precision, facilitates informed clinical decision-making, and has the potential to improve patient outcomes.

Methods A machine learning model powered by advanced AI algorithms was developed using breast imaging data to support breast cancer diagnosis. The model was trained and evaluated on a dataset comprising 3,245 patients diagnosed with breast cancer, sourced from hospitals and medical clinics across the United States. For model development, 50% of the data was randomly selected for training, while the remaining 50% was used to test and validate the model’s diagnostic performance.

Results The developed machine learning model accurately diagnosed 90.2% of the breast cancer using the testing datasets, exceeding currently published models.

Conclusion Artificial intelligence in breast imaging offers a valuable tool to enhance the early and accurate diagnosis of breast cancer, supporting healthcare professionals around the world. This technology has the potential to significantly improve patient outcomes—particularly in resource-limited settings with shortages of specialized medical personnel—and to advance breast cancer care on a global scale.