Avoiding Radiology’s Boeing 737 Max moment

Recently, Kemper et al. published an insightful paper on the challenges and opportunities of health technology assessment (HTA) models to evaluate the value, safety, and trustworthiness of radiology computer vision AI (RCVAI) tools used in daily practice.

Formal HTA frameworks are theoretically accurate and help define complex issues, but they are ideal approaches. The real world is messier. Ideal descriptions do not accurately represent what radiology or the tech worlds do when they develop, use, or assess AI.

Today, AI is a riotous environment of hype, rapid innovation, and an as-yet-unrealized promise to revolutionize radiology. The field is exploding, while our budgets and funding are not. Most healthcare organizations fund only technologies which, when assessed, show the most significant value or return on investment. Measuring the value of new healthcare technology is challenging and expensive, and most hospital systems lack resources and appropriate staff with HTA skills to assess AI thoroughly. RCVAI assessment is in a nascent state. We don’t know what RCVAI will be capable of, or what we would like it to do. Assessing RCVAI products is further complicated because they are changing faster than appropriate HTA can be designed and tested. On top of this, one needs to check the assessment tools themselves – how well do they work, and does using them improve health in some manner?

Unfortunately, RCVAI tools today are often brittle and work well in some situations but not others. Without robust assessment and monitoring, we will not know if an RCVAI tool we use is working correctly in our settings.

Suppose we are responsible for an AI tool that is used widely throughout our healthcare system, and that tool makes errors we don’t detect because we didn’t robustly assess the tool correctly at the outset, and aren’t monitoring intensively while it runs in our own setting? Many patients may be harmed, and we may be responsible for the equivalent of our own Boeing 737 Max episodes. This is something we simply cannot risk.

Article: Avoiding Boeing 737 Max moments: ideal versus real radiology AI assessment

Author: J. Raymond Geis

WRITTEN BY

  • Raymond Geis

    Senior Scientist, ACR Data Science Institute, Reston, Virginia, USA Adjunct Associate Professor of Radiology, National Jewish Health, Denver, Colorado, USA

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