Artificial intelligence (AI) isn’t a new term in radiology anymore – it’s been a buzzword at conferences for nearly half a decade. But, that doesn’t mean the technologies have been widely implemented. Industry experts agree there’s much still to learn about effectively designing these tools, and putting them into proper practice will require even more significant attention to detail.
Given that AI is still largely uncharted territory, radiology has the opportunity to learn from the mistakes other sectors have experienced. In particular, the airline industry has a long track record of using AI to improve safety. Consequently, when errors occur, gleaning the knowledge of what went wrong in order to side-step the same problems can be vital.
In recent months, two Boeing 737 MAX airplanes crashed due to AI failure. John Mongan, M.D. Ph.D., associate professor of radiology at the University of California at San Francisco, outlined the lessons radiology can learn from these tragedies in a March 18 article published in Radiology: Artificial Intelligence.
“Medicine should learn from aviation’s failures,” he wrote. “This is particularly true with respect to AI and automated systems, which are more broadly adopted in aviation than in medicine.”
To avoid making critical errors that could cause patient harm in the future, Mongan advised radiology practices and departments pursuing AI solutions to remember five key points:
1. Malfunctioning AI tools can create unforeseen safety hazards. The worst-case scenario of AI gone awry isn’t that your system simply won’t have AI – the consequence is potential real harm. For Boeing, this meant neither plane that crashed was equipped with optional sensors that could have alerted pilots to the malfunctioning safety system.
For radiology, Mongan said, this risk could come in the form of an improperly designed workflow algorithm that prioritizes cases from least urgent to most critical, rather than putting the most acute cases at the top of the worklist, potentially leading to significant harm.
“This may seem unlikely but would happen if the algorithm and implementation mismatched on whether the highest or lowest numeric priority value represents greatest acuity,” he said. “Adding AI introduces new possibilities for failure; risks of these failures must be identified and mitigated, which can be difficult prospectively.”
2. As much attention must be given to connecting inputs and outputs as is dedicated to algorithm development itself. Even though the 737’s safety system was designed correctly, crashes still occurred due to incorrect input data. Similarly, in radiology, training an AI tool on low-quality data can decrease the quality of patient care.
Consequently, it’s critical to test not only isolated algorithms, but the fully integrated system, he said. And, set up an alert that can be easily communicated if your AI system picks up on any inconsistent or potentially erroneous inputs.
“An alert should be clearly and reliably communicated to the people able to immediately address the issue,” he said. “These should be basic, required aspects of AI systems, not options or add-ons.”
3. Tell everyone when you add AI to your workflow, and train them. An AI tool can only work as well as the people charged with supervising, maintaining, and correcting it. The pilots and crew of the Boeing flights were unaware a safety