Improving mammography interpretation for both novice and experienced readers: a comparative study of two commercial artificial intelligence software

Objectives To evaluate the improvement of mammography interpretation for novice and experienced radiologists assisted by two commercial AI software. Methods We compared the performance of two AI software (AI-1 and AI-2) in two experienced and two novice readers for 200 mammographic examinations (80 cancer cases). Two reading sessions were conducted within 4 weeks. The readers rated the likelihood of malignancy (range, 1–7) and the percentage probability of malignancy (range, 0–100%), with and without AI assistance. Differences in AUROC, sensitivity, and specificity were analyzed. Results Mean AUROC increased in both novice (0.86 to 0.90 with AI-1 [ p = 0.005]; 0.91 with AI-2 [ p 0.999 with AI-1 and 0.282 with AI-2). There was no significant difference in the performance change depending on the type of AI software ( p > 0.999). Conclusion Commercial AI software improved the diagnostic performance of both novice and experienced readers. The type of AI software used did not significantly impact performance changes. Further validation with a larger number of cases and readers is needed. Clinical relevance statement Commercial AI software effectively aided mammography interpretation irrespective of the experience level of human readers. Key Points • Mammography interpretation remains challenging and is subject to a wide range of interobserver variability. • In this multi-reader study, two commercial AI software improved the sensitivity of mammography interpretation by both novice and experienced readers. The type of AI software used did not significantly impact performance changes. • Commercial AI software may effectively support mammography interpretation irrespective of the experience level of human readers.