为了评估在两种商业 AI 软件的协助下，新手和经验丰富的放射科医生对乳房 X 光检查判读的改进情况。我们比较了两种 AI 软件（AI-1 和 AI-2）在两名经验丰富的读者和两名新手读者中进行 200 次乳房 X 光检查（80 例癌症）的性能例）。 4 周内进行了两次阅读课程。读者对有或没有人工智能辅助的情况下，对恶性肿瘤的可能性（范围，1-7）和恶性肿瘤的百分比概率（范围，0-100%）进行了评分。分析了 AUROC、敏感性和特异性的差异。新手（AI-1 为 0.86 至 0.90 [p = 0.005]；AI-2 为 0.91 [p < 0.001]）和有经验的读者（AI-1 为 0.87 至 0.92）的平均 AUROC 均有所增加。 AI-1 [p < 0.001]；AI-2 为 0.90 [p = 0.004]）。新手读者中，AI-1 的敏感性从 81.3% 增加到 88.8% (p = 0.027)，AI-2 的敏感性增加到 91.3% (p = 0.005)，而 AI-1 的敏感性从 81.9% 增加到 90.6% (p = 0.001)，到在有经验的读者中，87.5% 的人使用 AI-2 (p = 0.016)。新手（均 p > 0.999）和有经验的读者（AI-1 的 p > 0.999，AI-2 的 0.282）的特异性均没有显着下降。根据人工智能软件的类型，性能变化没有显着差异（p > 0.999）。商业人工智能软件提高了新手和有经验的读者的诊断性能。使用的人工智能软件类型并没有显着影响性能变化。需要对大量病例和读者进行进一步验证。无论人类读者的经验水平如何，商业人工智能软件都可以有效地辅助乳房 X 线摄影解读。• 乳房 X 线摄影解读仍然具有挑战性，并且受到广泛的观察者间差异的影响。 • 在这项多读者研究中，两种商业人工智能软件提高了新手和经验丰富的读者对乳房X 线摄影解读的灵敏度。使用的人工智能软件类型并没有显着影响性能变化。 • 无论人类读者的经验水平如何，商业人工智能软件都可以有效地支持乳房X 线摄影解读。© 2023。作者，获得欧洲放射学会的独家许可。

To evaluate the improvement of mammography interpretation for novice and experienced radiologists assisted by two commercial AI software.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.Mean AUROC increased in both novice (0.86 to 0.90 with AI-1 [p = 0.005]; 0.91 with AI-2 [p < 0.001]) and experienced readers (0.87 to 0.92 with AI-1 [p < 0.001]; 0.90 with AI-2 [p = 0.004]). Sensitivities increased from 81.3 to 88.8% with AI-1 (p = 0.027) and to 91.3% with AI-2 (p = 0.005) in novice readers, and from 81.9 to 90.6% with AI-1 (p = 0.001) and to 87.5% with AI-2 (p = 0.016) in experienced readers. Specificity did not decrease significantly in both novice (p > 0.999, both) and experienced readers (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).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.Commercial AI software effectively aided mammography interpretation irrespective of the experience level of human readers.• 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.© 2023. The Author(s), under exclusive licence to European Society of Radiology.