Shaping the future of radiology with AI: Expert insights
By Evis Sala , MD, PhD, FRCR, FRCP | Chiara Bucciarelli Ducci , MD, PhD, FESC | Lisandro Paganini | Barry D. Pressman , MD | Geraldine B. McGinty ∙ May 01 2025 ∙ Duration 4:45
The next decade in radiology is poised for transformative advancements, driven largely by the integration of Artificial Intelligence (AI). From improving diagnostic accuracy to enabling seamless workflows and expanding global access to care, these innovations promise a future where radiologists play a more collaborative, strategic role in healthcare delivery. In recent conversations with industry leaders, several emerging trends surfaced as key to shaping this future for accessible, efficient imaging services.
At-a-glance:
One of the most pressing challenges in radiology is achieving a seamless workflow - a workflow that minimizes bottlenecks, reduces time-consuming tasks, and ultimately benefits both radiologists and patients. AI stands at the forefront of radiology’s future, not just for interpreting images, but as a powerful force to streamline entire diagnostic workflows.
AI is already showing its potential in areas like image interpretation, where it can assist in making diagnoses faster and more accurate. However, the real game-changer will be AI's ability to improve entire imaging workflows, from patient scheduling and scan execution to reporting and follow-ups. By automating repetitive tasks, AI will allow radiologists to focus on more complex aspects of patient care, leading to better outcomes and more efficient use of time.
“When I think about the future of radiology, the top of mind is definitely AI.”
Geraldine B. McGinty, MD
Radiologist United States of America
Simpler, faster scans will enhance patient experience, while radiologists benefit from decision-support tools that let them focus more on complex interpretation and collaborative care.
Another critical shift will be the changing role of radiologists themselves. As newer generations enter the field with AI training from the outset, the profession is set for a generational upgrade in capability and perspective. This transition won’t just be technological; it’s educational and cultural. Radiologists will become more integrated into multidisciplinary teams, working alongside other specialists and contributing to data-driven, predictive models of care. Their role will expand beyond image reading, focusing on high-value interpretation and broader healthcare collaboration, reshaping the specialty's identity in a more interconnected system.
“AI will help radiology by speeding up image processing and managing the vast number of images. If AI can filter out irrelevant images, we can focus on the important ones, making the process more efficient and valuable with fewer images to review.”
Barry D. Pressman, MD
Chair and Professor Department of Imaging United States of America
Additionally, leaders consider that this topic is not just about technology, it’s also about accessibility and expanding the reach of diagnostic imaging. One of the major trends identified by experts is the potential for AI to democratize access to radiology, particularly in underserved or resource-limited areas. Many regions still lack sufficient imaging infrastructure or trained professionals, creating stark disparities in care. AI will help close the imaging gap in regions with insufficient infrastructure or radiology professionals by automating image triage and improving the speed and accuracy of diagnoses.
As these technological advancements continue, radiology will become more efficient, more accessible, and a more integral part of collaborative, patient-centered care.
“With just one click, we can access all the AI tools we use daily. The integration of these tools can help reduce mistakes and streamline our process.”
Evis Sala MD, PhD, FRCR, FRCP
Professor and Chair, Department of Radiology Italy
Despite the promising potential of AI, significant barriers remain. Regulatory issues, high development costs, and the need for algorithm validation are among the main obstacles to slowing its widespread adoption. Some AI algorithms perform well in specific settings but struggle to maintain their effectiveness when applied to different patient demographics. These challenges must be addressed for AI to realize its full potential in improving healthcare. As these issues are resolved, AI's ability to enhance diagnostics, reduce healthcare costs, and extend access to care will become increasingly realized, making it a crucial part of the future healthcare landscape.
Disclaimer
Results are specific to the institution where they were obtained and may not reflect the results achievable at other institutions.
