ArticleEuropean radiology2023
Learning from the machine: AI assistance is not an effective learning tool for resident education in chest x-ray interpretation.
Article in European radiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 2 of them syntheses that pooled it.
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Who cites it
8 citing papers in PubMed, 2 syntheses or guidelines pooled it, 17 citations in OpenAlex.
- Artificial intelligence (AI) in radiological paediatric fracture assessment: an updated systematic review.European radiology · 2025Pooled it
- A systematic review of the impact of artificial intelligence on educational outcomes in health professions education.BMC medical education · 2025Pooled it
- Teaching radiology in undergraduate programmes in medical schools in francophone sub-Saharan Africa : a multicenter survey.BMC medical education · 2026Article
- Artificial intelligence in medical education for pulmonary nodule management: a narrative review.Translational lung cancer research · 2025Review
- Opinion paper: artificial intelligence as a clinical ally in lung cancer detection-the case for Gleamer's ChestView.Frontiers in oncology · 2025Article
- Improved differentiation of cavernous malformation and acute intraparenchymal hemorrhage on CT using an AI algorithm.Scientific reports · 2024Article
- Leveraging artificial intelligence in radiology education: challenges and opportunities.European radiology · 2023Article
- The Constrained-Disorder Principle Assists in Overcoming Significant Challenges in Digital Health: Moving from "Nice to Have" to Mandatory Systems.Clinics and practice · 2023Review
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Authors and funding
15 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesTo assess whether a computer-aided detection (CADe) system could serve as a learning tool for radiology residents in chest X-ray (CXR) interpretation.
methodsEight radiology residents were asked to interpret 500 CXRs for the detection of five abnormalities, namely pneumothorax, pleural effusion, alveolar syndrome, lung nodule, and mediastinal mass. After interpreting 150 CXRs, the residents were divided into 2 groups of equivalent performance and experience. Subsequently, group 1 interpreted 200 CXRs from the "intervention dataset" using a CADe as a second reader, while group 2 served as a control by interpreting the same CXRs without the use of CADe. Finally, the 2 groups interpreted another 150 CXRs without the use of CADe. The sensitivity, specificity, and accuracy before, during, and after the intervention were compared.
resultsBefore the intervention, the median individual sensitivity, specificity, and accuracy of the eight radiology residents were 43% (range: 35-57%), 90% (range: 82-96%), and 81% (range: 76-84%), respectively. With the use of CADe, residents from group 1 had a significantly higher overall sensitivity (53% [n = 431/816] vs 43% [n = 349/816], p < 0.001), specificity (94% [i = 3206/3428] vs 90% [n = 3127/3477], p < 0.001), and accuracy (86% [n = 3637/4244] vs 81% [n = 3476/4293], p < 0.001), compared to the control group. After the intervention, there were no significant differences between group 1 and group 2 regarding the overall sensitivity (44% [n = 309/696] vs 46% [n = 317/696], p = 0.666), specificity (90% [n = 2294/2541] vs 90% [n = 2285/2542], p = 0.642), or accuracy (80% [n = 2603/3237] vs 80% [n = 2602/3238], p = 0.955).
conclusionsAlthough it improves radiology residents' performances for interpreting CXRs, a CADe system alone did not appear to be an effective learning tool and should not replace teaching. CLINICAL RELEVANCE STATEMENT: Although the use of artificial intelligence improves radiology residents' performance in chest X-rays interpretation, artificial intelligence cannot be used alone as a learning tool and should not replace dedicated teaching. KEY POINTS: • With CADe as a second reader, residents had a significantly higher sensitivity (53% vs 43%, p < 0.001), specificity (94% vs 90%, p < 0.001), and accuracy (86% vs 81%, p < 0.001), compared to residents without CADe. • After removing access to the CADe system, residents' sensitivity (44% vs 46%, p = 0.666), specificity (90% vs 90%, p = 0.642), and accuracy (80% vs 80%, p = 0.955) returned to that of the level for the group without CADe.
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