Faculty, Staff and Student Publications
Language
English
Publication Date
5-27-2025
Journal
AMIA Summits on Translational Science Proceedings
DOI
10.1038/s44401-025-00023-6
PMID
42527464
PMCID
PMC13354153
PubMedCentral® Posted Date
5-27-2025
PubMedCentral® Full Text Version
Post-print
Abstract
The growing demand for imaging services in the U.S., driven by an aging population and the rise in chronic diseases, has contributed to a significant radiology workforce shortage. Simultaneously, supply-side constraints, including limited radiology residency positions and substantial retirements, have exacerbated this gap. These imbalances increase patient wait times, risk diagnostic delays, and contribute to radiologist burnout. Artificial intelligence (AI) offers potential solutions by addressing three primary areas: demand management, workflow efficiency, and capacity building. First, to manage demand, AI tools can leverage predictive analytics and decision-support systems to reduce unnecessary imaging and prioritize high-value imaging examinations. Next, AI can streamline tasks and boost efficiency with applications such as automated scheduling, assisted report generation, and image quality checks. Finally, by enhancing education, facilitating remote collaboration, improving patient communication, and offering advanced image interpretation assistance, AI can expand radiologists' capabilities, improve retention, and enhance long-term workforce sustainability. By integrating these approaches, radiology can address workforce shortages while upholding the highest standards of patient care.
Published Open-Access
yes
Recommended Citation
Jing, Andrew B; Garg, Naveen; Zhang, Jiajie; et al., "AI Solutions to the Radiology Workforce Shortage" (2025). Faculty, Staff and Student Publications. 1020.
https://digitalcommons.library.tmc.edu/uthshis_docs/1020