Ramli Hamid Marlina Tanty, Ab Mumin Nazimah, Abdul Hamid Shamsiah, Mohd Ariffin Natasha, Mat Nor Khariah, Saib Ernisha, Mohamed Nurul Amira
Department of Radiology, Faculty of Medicine, University Teknologi MARA, Sungai Buloh, Selangor, Malaysia.
PLoS One. 2025 May 7;20(5):e0322925. doi: 10.1371/journal.pone.0322925. eCollection 2025.
This study evaluates the impact of artificial intelligence (AI) assistance on the diagnostic performance of radiologists with varying levels of experience in interpreting mammograms in a Malaysian tertiary referral center, particularly in women with dense breasts.
A retrospective study including 434 digital mammograms interpreted by two general radiologists (12 and 6 years of experience) and two trainees (2 years of experience). Diagnostic performance was assessed with and without AI assistance (Lunit INSIGHT MMG), using sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and area under the receiver operating characteristic curve (AUC). Inter-reader agreement was measured using kappa statistics.
AI assistance significantly improved the diagnostic performance of all reader groups across all metrics (p < 0.05). The senior radiologist consistently achieved the highest sensitivity (86.5% without AI, 88.0% with AI) and specificity (60.5% without AI, 59.2% with AI). The junior radiologist demonstrated the highest PPV (56.9% without AI, 74.6% with AI) and NPV (90.3% without AI, 92.2% with AI). The trainees showed the lowest performance, but AI significantly enhanced their accuracy. AI assistance was particularly beneficial in interpreting mammograms of women with dense breasts.
AI assistance significantly enhances the diagnostic accuracy and consistency of radiologists in mammogram interpretation, with notable benefits for less experienced readers. These findings support the integration of AI into clinical practice, particularly in resource-limited settings where access to specialized breast radiologists is constrained.
本研究评估了在马来西亚一家三级转诊中心，人工智能（AI）辅助对不同经验水平的放射科医生解读乳房X光片诊断性能的影响，特别是对乳房致密的女性。
一项回顾性研究，纳入了由两名普通放射科医生（分别有12年和6年经验）和两名实习医生（有2年经验）解读的434份数字化乳房X光片。使用灵敏度、特异度、阳性预测值（PPV）、阴性预测值（NPV）和受试者操作特征曲线下面积（AUC），在有和没有AI辅助（Lunit INSIGHT MMG）的情况下评估诊断性能。使用kappa统计量测量阅片者间的一致性。
AI辅助在所有指标上均显著提高了所有阅片者组的诊断性能（p < 0.05）。资深放射科医生始终获得最高的灵敏度（无AI时为86.5%，有AI时为88.0%）和特异度（无AI时为60.5%，有AI时为59.2%）。初级放射科医生表现出最高的PPV（无AI时为56.9%，有AI时为74.6%）和NPV（无AI时为90.3%，有AI时为92.2%）。实习医生的表现最低，但AI显著提高了他们的准确性。AI辅助在解读乳房致密女性的乳房X光片时特别有益。
AI辅助显著提高了放射科医生解读乳房X光片的诊断准确性和一致性，对经验较少的阅片者有显著益处。这些发现支持将AI整合到临床实践中，特别是在资源有限、获取专业乳腺放射科医生受限的环境中。
