Heterogeneity and predictors of the effects of AI assistance on radiologists
Harvard University · Stanford University · +1 more institution
Abstract
The integration of artificial intelligence (AI) in medical image interpretation requires effective collaboration between clinicians and AI algorithms. Although previous studies demonstrated the potential of AI assistance in improving overall clinician performance, the individual impact on clinicians remains unclear. This large-scale study examined the heterogeneous effects of AI assistance on 140 radiologists across 15 chest X-ray diagnostic tasks and identified predictors of these effects. Surprisingly, conventional experience-based factors, such as years of experience, subspecialty and familiarity with AI tools, fail to reliably predict the impact of AI assistance. Additionally, lower-performing radiologists…
Citation impact
- FWCI
- 16.25
- Percentile
- 100%
- References
- 48
Authors
6Topics & keywords
- Subspecialty
- Artificial intelligence
- Applications of artificial intelligence
- Medicine
- Machine learning
- Scale (ratio)
- Computer science
- Medical physics