Department of Clinical Radiology, Great Ormond Street Hospital for Children, London, UK
UCL Great Ormond Street Institute of Child Health, Great Ormond Street Hospital for Children, London, UK.
BMJ. 2022 Dec 21;379:e072826. doi: 10.1136/bmj-2022-072826.
To determine whether an artificial intelligence candidate could pass the rapid (radiographic) reporting component of the Fellowship of the Royal College of Radiologists (FRCR) examination.
Prospective multi-reader diagnostic accuracy study.
United Kingdom.
One artificial intelligence candidate (Smarturgences, Milvue) and 26 radiologists who had passed the FRCR examination in the preceding 12 months.
Accuracy and pass rate of the artificial intelligence compared with radiologists across 10 mock FRCR rapid reporting examinations (each examination containing 30 radiographs, requiring 90% accuracy rate to pass).
When non-interpretable images were excluded from the analysis, the artificial intelligence candidate achieved an average overall accuracy of 79.5% (95% confidence interval 74.1% to 84.3%) and passed two of 10 mock FRCR examinations. The average radiologist achieved an average accuracy of 84.8% (76.1-91.9%) and passed four of 10 mock examinations. The sensitivity for the artificial intelligence was 83.6% (95% confidence interval 76.2% to 89.4%) and the specificity was 75.2% (66.7% to 82.5%), compared with summary estimates across all radiologists of 84.1% (81.0% to 87.0%) and 87.3% (85.0% to 89.3%). Across 148/300 radiographs that were correctly interpreted by >90% of radiologists, the artificial intelligence candidate was incorrect in 14/148 (9%). In 20/300 radiographs that most (>50%) radiologists interpreted incorrectly, the artificial intelligence candidate was correct in 10/20 (50%). Most imaging pitfalls related to interpretation of musculoskeletal rather than chest radiographs.
When special dispensation for the artificial intelligence candidate was provided (that is, exclusion of non-interpretable images), the artificial intelligence candidate was able to pass two of 10 mock examinations. Potential exists for the artificial intelligence candidate to improve its radiographic interpretation skills by focusing on musculoskeletal cases and learning to interpret radiographs of the axial skeleton and abdomen that are currently considered "non-interpretable."
确定人工智能是否能够通过皇家放射学院研究员（FRCR）考试的快速（影像学）报告部分。
前瞻性多读者诊断准确性研究。
英国。
一名人工智能候选人（Smarturgences，Milvue）和 26 名在前 12 个月内通过 FRCR 考试的放射科医生。
人工智能与放射科医生在 10 次 FRCR 快速报告模拟考试中的准确性和通过率（每次考试包含 30 张影像学照片，准确率需达到 90%才能通过）。
将无法解释的图像从分析中排除后，人工智能候选人的平均总体准确率为 79.5%（95%置信区间 74.1%至 84.3%），通过了 10 次 FRCR 模拟考试中的 2 次。平均放射科医生的准确率为 84.8%（76.1%至 91.9%），通过了 10 次模拟考试中的 4 次。人工智能的敏感性为 83.6%（95%置信区间 76.2%至 89.4%），特异性为 75.2%（66.7%至 82.5%），而所有放射科医生的汇总估计值分别为 84.1%（81.0%至 87.0%）和 87.3%（85.0%至 89.3%）。在 300 张影像学照片中，有 148 张被>90%的放射科医生正确解读，人工智能候选人在其中 14 张（9%）上出现错误。在 20 张大多数（>50%）放射科医生错误解读的影像学照片中，人工智能候选人在其中 10 张（50%）上是正确的。大多数成像陷阱与肌肉骨骼而非胸部影像学照片的解读有关。
当为人工智能候选人提供特殊豁免权（即排除无法解释的图像）时，人工智能候选人能够通过 10 次模拟考试中的 2 次。人工智能候选人有可能通过专注于肌肉骨骼病例并学习解读目前被认为“无法解释”的轴向骨骼和腹部影像学照片来提高其影像学解读技能。
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