AI Redefining Radiology: Challenges and Opportunities

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AI Redefining Radiology: Challenges and Opportunities
Artificial IntelligenceRadiologyWorkflow Optimization

New research from the Journal of the American College of Radiology (JACR) explores the impact of Artificial Intelligence (AI) on radiology practices. The research highlights the potential of AI to improve efficiency and patient care while also addressing challenges related to infrastructure, regulations, reimbursement, and integration.

American College of RadiologyMar 9 2026 Artificial intelligence has the potential to redefine how radiology is practiced, as well as highlight specific challenges for radiology departments, according to new research from the Journal of the American College of Radiology .

The JACR Focus Issue on Impact of AI on Workflow Optimization offers a collection of invited research and reviews that explore the ways in which AI technology is being regularly utilized across practice types. When thoughtfully implemented, AI can complement human expertise and improve efficiency and patient care. Successful workflow optimization requires the integration of AI technology into routine workflows. This can be hampered by insufficient infrastructure, strict institutional regulations, and a lack of insurance reimbursement. Poor integration of AI may degrade workflows, satisfaction, and safety and perpetuate bias in healthcare." Gelareh Sadigh, MD, Associate Editor for Health Services Research, said Journal of the American College of Radiology According to Dr. Sadigh, the articles included in this focus issue reflect a broader shift in radiology; workflow isn't a secondary benefit of AI - it's the main determinant of whether a tool is successful or not. If AI is going to meaningfully help radiology, it must make care delivery better and not more complicated. "This focus issue provides meaningful signposts for AI effectiveness as we navigate a rapidly shifting landscape," said Ruth C. Carlos, MD, MS, Editor-in-Chief, Journal of the American College of Radiology.Journal reference:Sadigh, G., et al. . From Image Overload to Intelligent Workflows. Science Immunology. DOI: 10.1016/j.jacr.2026.01.001, https://www.jacr.org/article/S1546-144000001-3/fulltext.

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