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Can artificial intelligence pass the Fellowship of the Royal College of Radiologists examination? Multi-reader diagnostic accuracy study.

Published version
Peer-reviewed

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Authors

Shelmerdine, Susan Cheng  ORCID logo  https://orcid.org/0000-0001-6642-9967
Shamshuddin, Sameer  ORCID logo  https://orcid.org/0000-0002-4546-1762
Weir-McCall, Jonathan Richard  ORCID logo  https://orcid.org/0000-0001-5842-842X

Abstract

OBJECTIVE: 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. DESIGN: Prospective multi-reader diagnostic accuracy study. SETTING: United Kingdom. PARTICIPANTS: One artificial intelligence candidate (Smarturgences, Milvue) and 26 radiologists who had passed the FRCR examination in the preceding 12 months. MAIN OUTCOME MEASURES: 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). RESULTS: 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. CONCLUSIONS: 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."

Description

Peer reviewed: True


Funder: National Institute for Health and Care Research; FundRef: http://dx.doi.org/10.13039/501100000272

Keywords

Humans, Artificial Intelligence, Fellowships and Scholarships, Prospective Studies, Radiologists, Radiography, Retrospective Studies

Journal Title

BMJ

Conference Name

Journal ISSN

0959-8146
1756-1833

Volume Title

Publisher

BMJ
Sponsorship
National Institute for Health Research (IS-BRC-1215-20014)