Radiology journal devotes entire issue to whether AI actually helps or hinders clinical workflows

The promise of artificial intelligence in radiology has always been specific: faster reads, fewer missed findings, more efficient workflows. But there is a growing recognition that the technology's success depends less on algorithmic performance and more on how well it fits into the daily reality of clinical practice.

The Journal of the American College of Radiology (JACR) has published a focus issue dedicated entirely to this question. The collection of invited research and reviews examines how AI tools are being used across different radiology practice types -- and where they are falling short.

Workflow as the deciding factor

The issue's central argument is straightforward: workflow integration is not a secondary benefit of AI in radiology. It is the primary determinant of whether a tool succeeds or fails.

"Successful workflow optimization requires the integration of AI technology into routine workflows," said JACR Associate Editor Gelareh Sadigh, MD. "This can be hampered by insufficient infrastructure, strict institutional regulations, and lack of insurance reimbursement. Poor integration of AI may degrade workflows, satisfaction, and safety and perpetuate bias in healthcare."

That last point is worth pausing on. AI tools that are poorly integrated into clinical workflows do not simply fail to help -- they can actively make things worse. Alert fatigue from excessive notifications, disrupted reading patterns, and systems that add steps rather than eliminate them all represent real risks documented across the articles in this issue.

The gap between capability and utility

Radiology has been one of the earliest and most enthusiastic adopters of AI in medicine. Algorithms can detect lung nodules, flag stroke on CT scans, and prioritize urgent cases in reading queues. Many of these tools perform well in controlled evaluations.

But clinical environments are not controlled evaluations. Radiologists work within PACS systems, electronic health records, and institutional protocols that were not designed with AI integration in mind. Adding an AI tool to this ecosystem requires attention to interface design, alert management, reporting workflows, and the interactions between AI outputs and existing clinical decision-making patterns.

The focus issue reflects what Sadigh described as a broader shift in how the field thinks about AI: the conversation has moved from "can AI do this?" to "does AI actually make care delivery better when implemented in real practice?"

Infrastructure and reimbursement barriers

Two practical obstacles surface repeatedly in the collection. First, many radiology departments lack the technical infrastructure to deploy AI tools effectively -- the computing resources, network bandwidth, and IT support needed to run algorithms in real time alongside existing systems. Second, insurance reimbursement for AI-assisted reads remains inconsistent, creating financial uncertainty for practices considering adoption.

These are not technical problems in the traditional sense. They are organizational and economic problems that determine whether technically capable AI tools ever reach clinical use at scale.

What the issue does not resolve

The JACR focus issue is a collection of perspectives and reviews, not a definitive resolution. It does not provide standardized metrics for evaluating workflow integration, nor does it offer a consensus framework for AI deployment. The articles represent different practice environments, different AI tools, and different definitions of success.

"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 of JACR. Signposts, not a roadmap -- a distinction that reflects where the field currently stands.

For radiology departments weighing AI adoption, the collection's most useful contribution may be its candid assessment of what can go wrong. The published literature on AI in radiology skews heavily toward capability demonstrations. This issue shifts attention to the implementation challenges that determine whether those capabilities translate into clinical value.