A heated Twitter conversation revealed widespread discontent with imaging’s outdated and fragmented IT infrastructure, suggesting that it’s draining radiologist productivity and standing in the way of AI adoption.
This tweet by Memorial Sloan Kettering’s Anton Becker, MD, PhD got things started: “95% of radiology departments would do well to direct 100% of their AI efforts and budget towards upgrade and maintenance of PACS, RIS and dictation software for the next 5 years… Our field is plagued by legacy software.”
And here’s what the ensuing replies and retweets revealed:
- PACS Productivity – Nearly everyone agreed that their overall imaging IT setup was insufficient, with one rad estimating that a “supercharged PACS” would improve his productivity by 30%, and another noting that workflow customization would “at least double” her speed and accuracy.
- Imaging IT Revolution – Some called upon the “legacy” PACS, RIS, and voice recognition vendors to make more “revolutionary changes,” rather than settling for tweaks to current setups. Others proposed government intervention.
- IT Isn’t Flashy – One thing that might be holding some imaging IT overhauls back is “it’s not as flashy to boast” about high-quality infrastructure, and “the people who have authority to allocate resources are more motivated by flash than function.”
- Holistic IT – Eventually the conversation led to several well received proposals that we “eliminate the idea of PACS as a category and start thinking more holistically about radiology IT.” In other words, this might be more of a “fragmentation problem” than a PACS/RIS/voice functionality problem (or an AI budget problem).
The Takeaway
Even if RadTwitter tends to skew towards academic radiologists and often focuses on what’s going wrong, this conversation indicates widespread dissatisfaction with current imaging IT setups, and suggests that radiologist productivity (and perhaps accuracy and burnout) would improve significantly if imaging IT worked as they’d like it to work.
It’s debatable whether this imaging IT problem is actually due to an unnecessary focus on AI (very little of the conversation actually focused on AI), but it does seem reasonable that rad teams with solid infrastructure would be more likely to embrace AI.
