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The Effect of AI on the Radiologist Workforce: A Task-Based Analysis
1Professor of Radiology, Medicine, and Biomedical Data Science, Senior Associate Vice Provost for Research, Senior Fellow, Institute for Human-Centered Artificial Intelligence, Stanford University.
Background:
The effect of AI algorithms on the radiology workforce has been a subject of commentary and controversy. There is now sufficient published evidence to support a quantitative task-based analysis to predict these effects.
Purpose:
To construct a quantitative, task-based model to predict the effect of AI on the radiology workforce using the best available evidence.
Materials And Methods:
We reviewed the literature to establish the tasks on which radiologists spend their time. We then developed categories of AI applications that could affect these tasks. We used published evidence to estimate the effect of each AI application on each radiology task using a 5-year time horizon. When published evidence was unavailable, we used our own judgment.
Results:
The model projects a 33% reduction in hours worked by radiologists in 5 years, with a range of 14% to 49%. The main effects are due to radiology report drafting for all modalities and study delegation for radiography and mammography.
Conclusion:
AI applications likely will cause a significant decrease in radiologist hours worked.. Given the relatively static radiology workforce and the continued growth in imaging volumes, radiologist job loss is unlikely for the foreseeable future.
