Why all AI strategies need an imaging informaticist
Discussions about AI and radiology often focus on the researchers who help develop the algorithms and radiologists themselves. But a new analysis published in Academic Radiology shines a light on another key role in the implementation of AI: the imaging informaticist.
“An imaging informaticist is a unique individual who sits at the intersection of clinical radiology, data science and information technology,” wrote author Tessa S. Cook, MD, PhD, of the University of Pennsylvania in Philadelphia. “With the ability to understand each of the different domains and translate between the experts in these domains, imaging informaticists are now essential players in the development, evaluation and deployment of AI in the clinical environment.”
As AI research has escalated in recent years, data preparation has become an almost underappreciated aspect of the entire process. In fact, Cook noted, collecting, validating, labeling, converting and deidentifying data takes more time and effort than actually programming the algorithm being used. This is where the imaging informaticist comes in: he or she can help own that data and provide important leadership when it comes to solving problems and moving forward.
“Domain expertise is critical to the success and adoption of AI tools, both within and outside medicine,” Cook wrote. “Within radiology in particular, data scientists must learn both the clinical context for the problem being addressed as well as the technical aspects of the data, how it is created and stored, how to consume it and what it represents. Both radiologists and imaging informaticists make important contributions to the development of imaging-based AI tools, not only by lending their respective, necessary expertise, but also by critically evaluating the resulting tools for both clinical accuracy and likelihood of successful deployment in the clinical workflow.”
Imaging informaticists also provide value by helping evaluate an AI model. Sure, the initial research might indicate a model can achieve a high accuracy or area under the ROC curve—but has it been properly tested on external data? Is it truly “thinking” as a radiologist would in the same scenario? These are just some of the questions imaging informaticists might ask while confirming the validity of a given research project.
On a related note, imaging informaticists can also help healthcare providers integrate AI technologies into their day-to-day workflow.
“Multiple informatics considerations come into play during the deployment process,” Cook wrote. “The tool may reside within the facility (i.e., ‘on prem,’ or on the premises) or in the cloud. Each option has its advantages and disadvantages in terms of data security, processing speed, and hardware and software requirements, and different configurations may be needed at different locations within the same practice.”
The relationship between AI and radiology is only going to grow in the years ahead. Cook concluded her analysis by saying some radiologists will now be need “to learn yet another skill set and body of knowledge in order to use this technology to improve the way we care for our patients.”
“It is important to leverage existing expertise of the imaging informaticists in our community, as well as train a pipeline of future such experts, if we aim to remain relevant in this space,” she wrote. “It is our responsibility as radiologists and imaging informaticists to ensure that this new technology functions as expected, does not harm our patients and improves the quality, efficiency, availability of and access to care for our patients.”
Michael has more than 18 years of experience as a professional writer and editor. He has written at length about cardiology, radiology, artificial intelligence and other key healthcare topics.