The integration of contextflow SEARCH Lung CT was a step-by-step process. Romeijn: “At first, we had to open a link in our PACS, directing us to a separate contextflow viewer. That was a nice integration by itself.” But the wish of the end users remained to have as much integration with the Sectra PACS as possible.
Another improvement in the contextflow – Sectra integration is how its output can be transferred to reports. Lamb: “I [previously] couldn’t transfer the results from the analysis directly into my report. No software had this possibility. So we all asked for that.” Now it’s more a matter of checking and accepting the output before it is added to a report. This also prevents errors.
The application itself is also constantly improving. “We know that they thought carefully about longitudinal analyses. This feature has not yet been implemented, but it’s coming after this summer,” Romeijn explains. From then on, contextflow can also be used to properly visualize and quantify the development of lung abnormalities over time. Lamb: “This is what we told them from the beginning. We need to follow lung nodules over time, visualize them using graphs, doubling times, and more. They have developed this feature exactly as we want it in daily use. This development is truly a win-win. Collaboration offers you the best solutions.” According to Romeijn, it can be a challenge to test what works best in clinical practice. For example; how to choose which previous scans and series should be compared with the latest scan.
The impact of working with AI
What impact does working with AI have on radiologists? Lamb says this is difficult to say, “because how do you measure this impact?” In the Radiology AI Lab, he and his team are developing a method to quantify how focused radiologists stay in their tasks. “As radiologists, we take very few breaks. We sometimes start at 8 AM and go home at 8 PM, which shouldn’t be possible. We want to discover the parameters that are most informative about your reliability, efficiency, but above all, your job satisfaction.” This shows that working with tools like contextflow goes far beyond saving time and improving quality of care, but also about job satisfaction and preventing burnout.
Patient communication remains essential when working with AI. “They have access to their EHR (Electronic Health Record) data, but the wording is often very technical and medical. People can start panicking. We want to take that into account by having a simple patient explanation in the future,” Lamb says. Romeijn adds that if there is a difference between the AI output and what the radiologist sees, the radiologist should let the patient know why the difference exists and what findings prevail.
At LUMC, referring clinicians have rapidly become used to the radiological output with the help of AI. Lamb explains that they see the luxury of it. “Even though it has taken some time to set up, it is a very efficient, quantitative way of communicating.” According to Grootjans, it is “vital to further involve the referring clinicians so that you keep looking at the workflow holistically. What information are you providing, and what is necessary?” Of course, the human factor will always be needed. Lamb: “I am not worried about that at all. I hope that everything will be automated at some point and that radiologists will translate this into clinical practice. They will become [more like] imaging consultants and can guide patients toward their next steps.”