AI-Radiologist Team More Accurate Than Either Alone
As more studies show that AI algorithms can match (or sometimes even surpass) the accuracy of trained radiologists on a variety of conditions, many have warned that deep learning models may replace radiology readers. However, a new study published in IEEE Transactions on Medical Imaging suggests that having an experienced radiologist whose reads can be enhanced by AI techniques may be the best option for identifying breast cancer screenings.
Researchers at New York University, hailing both from the NYU School of Medicine and the NYU Center for Data Science, developed a deep convolutional neural network program to screen mammography images for breast cancer. The system was trained and evaluated on a data set of more than 1,000,000 images and could accurately detect cancer at a rating of approximately 89.5%.
When they compared the AI program to a group of 14 experienced radiologists, they showed that the model was as accurate as the doctors, which falls in line with many of the AI vs. radiologist studies currently flooding the literature. However, beyond validating the model, the researchers wanted to see the benefits of a hybrid reading model, where the program could average the probability of a malignancy predicted by the radiologist with the prediction from the neural network. When both the AI and the radiologist worked together, the accuracy of reads increased to 90%—suggesting medicine may benefit from pairing experienced readers with proven AI tools.