A group of radiology leaders starred in Canon Medical’s recent State of AI in Radiology Today Roundtable, sharing insights into how imaging AI is being used, where it’s needed most, and how AI might assume a core role in medical imaging.
The panelists were largely from the user/clinical side of imaging (U of Maryland’s Eliot Siegel, MD; UC Irvine’s Peter Chang, MD; UHS Delaware’s Cindy Siegel, CRA; U of Toronto’s Patrik Rogalla, MD; and Canon’s Director of Healthcare Economics Tom Szostak), with deeper AI experience than many typical radiology team members.
Here are some of the big takeaways:
We’re Still Early – The panel started by making sure everyone agrees on the definition of AI and much of ensuing discussions focused on AI’s future potential, which says a lot about where we are in AI’s lifecycle.
Do We Need AI? – The panelists agreed that radiology does indeed need AI, largely because it can improve the patient experience (shorter scans, faster results, fewer call-backs), help solve radiology’s inefficiency problems, and improve diagnostic accuracy.
Does AI Really Improve Efficiency? – Outside of image reconstruction, none of the panelists were ready to say that AI currently makes radiologists faster. However, they still believe that AI will improve future radiology workflows and outcomes.
Finding The Killer App – Things got a lot more theoretical at the halfway point, when the conversation shifted to what “killer apps” might bring imaging AI into mainstream use, including AI tools that:
- Identify and exclude normal scans with extremely high accuracy (must be far more accurate than humans and limit false positives)
- Curate and submit all CMS quality reporting metrics (eliminates admin work, generates revenue)
- Identify early-stage diseases for population health programs (keeps current diagnostic workflows intact)
- Interpret and diagnose all X-ray exams (eliminates high volume/repetitive exams, rads don’t read some XRs in many countries)
- Improve image quality, allow faster scans, reduce dosage (aka DL image reconstruction)
AI’s Radiologist Impact – The panelists don’t see AI threatening radiologist jobs in the short to mid-term given AI’s current immaturity, the “tremendous inefficiencies” that still exist in radiology, and the pace of imaging volume growth. They also expect volume growth to drive longer term demand for both AI and rads, suggesting that AI adoption might even amplify future volume growth (if AI expands bandwidth and cuts interpretation costs, the laws of economics suggest that more scans would follow).
What AI Needs – With most of the technical parts of building algorithms now figured out, AI’s evolution will depend on getting enough training data, improving how AI is integrated into workflows, and making sure AI is solving radiology’s biggest problems. Imaging AI also needs healthcare to be open to change, which would require clear clinical, operational, and financial upsides.