Abdominal radiology (New York) · 2026
When intelligence meets radiology: the dual impact of ai on radiologists' workload, burnout, and economic value.
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Ghasemi Ali, Etemadimanesh Azin, Chhabra Avneesh
Аннотация
PURPOSE: To examine the impact of artificial intelligence (AI) on radiologists' workload and economic outcomes by synthesizing current evidence on workflow efficiency, burnout, and compensation dynamics.
METHODS: This narrative review synthesized peer-reviewed original studies, systematic reviews, and meta-analyses identified through a structured literature search of PubMed, focusing on clinical efficiency, occupational stress, economic value, and policy readiness across diagnostic, administrative, and informatics interventions.
RESULTS: AI technologies have demonstrated benefits in triaging normal examinations, reducing turnaround times, and automating administrative tasks; for example, a single-center prospective intervention integrating a templated EHR-PACS interface reduced average report turnaround time from 11.2 to 6.9 min (a 38% reduction). However, AI adoption has also been associated with increased cognitive and oversight demands, with cross-sectional data showing correlations between AI use and emotional exhaustion. Economic models suggest potential cost savings, but outdated reimbursement frameworks limit financial returns to radiologists, and workforce surveys reveal gaps in AI education and concerns about compensation fairness.
CONCLUSION: AI offers promising avenues to reduce routine workload and enhance diagnostic efficiency, yet without systemic reforms to reimbursement, training, and policy, its benefits may be unevenly distributed or counterbalanced by rising oversight burdens. Future research should prioritize longitudinal and multicenter studies with standardized outcomes to guide sustainable integration of AI in radiology practice.
METHODS: This narrative review synthesized peer-reviewed original studies, systematic reviews, and meta-analyses identified through a structured literature search of PubMed, focusing on clinical efficiency, occupational stress, economic value, and policy readiness across diagnostic, administrative, and informatics interventions.
RESULTS: AI technologies have demonstrated benefits in triaging normal examinations, reducing turnaround times, and automating administrative tasks; for example, a single-center prospective intervention integrating a templated EHR-PACS interface reduced average report turnaround time from 11.2 to 6.9 min (a 38% reduction). However, AI adoption has also been associated with increased cognitive and oversight demands, with cross-sectional data showing correlations between AI use and emotional exhaustion. Economic models suggest potential cost savings, but outdated reimbursement frameworks limit financial returns to radiologists, and workforce surveys reveal gaps in AI education and concerns about compensation fairness.
CONCLUSION: AI offers promising avenues to reduce routine workload and enhance diagnostic efficiency, yet without systemic reforms to reimbursement, training, and policy, its benefits may be unevenly distributed or counterbalanced by rising oversight burdens. Future research should prioritize longitudinal and multicenter studies with standardized outcomes to guide sustainable integration of AI in radiology practice.