In a study comparing AI approaches for breast cancer detection across structured clinical datasets and imaging datasets, performance varied sharply by data type: machine learning models reached up to 98% accuracy on structured data, while deep learning models led image tasks with 97.88% accuracy on histopathology and 92.00% on ultrasound. The study also found calibration differed by model family, with Logistic Regression, Gradient Boosting, and Random Forest among the best-calibrated structured-data models, while transfer-learning deep networks were better calibrated on histopathology images.

Why It Matters To Your Practice

• AI may be useful as a complementary diagnostic support tool when breast imaging is inconclusive or reader variability is a concern.

• The findings suggest one model type is no longer enough for breast cancer AI across workflows; performance depends heavily on whether the input is clinical tabular data or images.

• Calibration matters clinically because well-calibrated probability estimates may better support risk discussions and downstream decisions.

Clinical Implications

• For structured clinical data, ensemble and probabilistic machine learning models performed strongly, with accuracy up to 98%.

• For imaging tasks, deep learning models outperformed traditional machine learning, especially on histopathology images and ultrasound.

• Logistic Regression, Gradient Boosting, and Random Forest showed stronger calibration than Decision Tree and Naive Bayes on structured datasets.

• Transfer-learning architectures appeared better calibrated than a from-scratch CNN baseline and some models tested on the smaller BUSI ultrasound dataset.

Insights

• The analysis covered multiple structured datasets, including the Wisconsin Breast Cancer Dataset, CSAW-CC, and SEER, plus mammography, histopathology, and ultrasound image datasets.

• Deep learning models assessed included CNN, ResNet50, VGG16, and DenseNet.

• All reported performance metrics included 95% confidence intervals, and calibration was assessed with Expected Calibration Error and reliability diagrams.

• Smaller datasets appeared more vulnerable to weaker calibration and less consistent model performance.

The Bottom Line

• AI for breast cancer detection is promising, but clinicians should match the model approach to the data source rather than expect one system to generalize across all settings.

• Prospective validation across diverse patient populations is still needed before broad clinical adoption.