Workload of diagnostic radiologists in the foreseeable future based on recent (2024) scientific advances: Updated growth expectations

Abstract

PURPOSE: To assess the expected impact of the 2024 medical imaging literature on the workload of diagnostic radiologists. METHODS: A random sample of 416 articles on diagnostic imaging that was published in 2024 was reviewed by one radiologist working in an academic tertiary care center and another radiologist working in a non-academic general teaching hospital. RESULTS: In the academic tertiary care hospital setting, 56.5 % (235/416) of articles had the potential to directly impact patient care, of which 48.9 % (115/235) would increase workload, 48.1 % (113/235) would not change workload, 0.4 % (1/235) would decrease workload, and 2.6 % (6/235) had an unclear effect on workload. Studies with Artificial Intelligence (AI) as primary research area were significantly (P < 0.001) more likely to increase workload compared to studies with another primary research area, with an Odds Ratio (OR) of 14.3 (95 % confidence interval [CI]: 4.2 to 48.2). In the non-academic general teaching hospital setting, 56.5 % (231/416) of articles had the potential to directly impact patient care, of which 48.9 % (113/231) would increase workload, 48.1 % (111/231) would not change workload, 0.4 % (1/231) would decrease workload, and 2.6 % (6/231) had an unclear effect on workload. Studies with AI as primary research area were significantly (P < 0.001) more likely to increase workload compared to studies with another primary research area, with an OR of 13.7 (95 % CI: 4.1 to 46.5). CONCLUSION: The workload of diagnostic radiologists is expected to increase based on recent (2024) scientific literature, and AI applications generally seem to have an aggravating effect on workload.

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