Hospitals are racing to plug artificial intelligence into radiology, but a quieter question keeps surfacing: how do you know the AI is actually working once it's running on real patients? A report from AuntMinnie, covering the annual meeting of the Society for Imaging Informatics in Medicine (SIIM), points to one practical answer.
According to AuntMinnie, a supplemental PACS can help validate AI imaging workflows. PACS — short for Picture Archiving and Communication System — is the software backbone that stores, displays, and routes medical images like X-rays, CT scans, and MRIs. It's already core infrastructure in virtually every radiology department.
The idea highlighted at SIIM, as reported by AuntMinnie, is to use an additional or secondary PACS not as the primary clinical viewer, but as a checkpoint for the AI tools layered into imaging workflows. In practice, that means having a dedicated environment where AI outputs can be observed, compared, and confirmed against expectations before or alongside their use in patient care.
The source material here is limited to AuntMinnie's headline-level coverage from the conference, so the finer technical details — vendors, specific setups, and measured results — aren't spelled out in what's available. What is clear is the framing: validation, not just deployment, is being treated as part of the AI rollout conversation among imaging informatics professionals.
Why it matters: as AI moves from pilot projects into everyday diagnosis, the industry is signaling that trustworthy medicine depends not only on smart algorithms but on the unglamorous infrastructure that lets clinicians check whether those algorithms can be trusted.