Christoph Wald, M.D., Ph.D, MBA, FACR, vice chair of the ACR Board of Chancellors and chair of the ACR Commission on Informatics, detailed the challenges ARCH-AI is set to address and how the framework can help providers navigate future innovations in medical AI.
A key aspect of ARCH-AI involves helping healthcare organizations address obstacles to AI integration in radiology. These obstacles are related to legacy infrastructure, functionality limitations and information exchange.
Wald emphasized that conventional infrastructure and solutions deployed in radiology departments, such as radiology information systems (RIS), picture archiving and communication systems (PACS), voice recognition and other systems, were created when AI tools were not yet widely available.
"Many of these systems were not designed to natively or easily integrate with the AI systems that provide specific outputs," he explained. "For example, it is not easy to make AI results visible in a traditional PACS system without first archiving the result in PACS together with the actual patient data."
"While this is a workaround to make a result visible on the PACS, this is not advisable," he continued. "Especially not when the result comes from a nondiagnostic, triage-only cleared AI algorithm designed and tested solely to prioritize workflow based on the presence of a certain imaging observation -- which do not equal a diagnosis." He further noted that many imaging practices have devised these workarounds and custom integration solutions in an effort to efficiently incorporate AI results into routine workflows.
"Those custom solutions for interaction with AI output are built around legacy technical obstacles but are not necessarily optimized -- yet -- for usability and efficiency, and [they] are not necessarily available to smaller practices or those without designated IT teams or development resources," Wald stated.
To combat this, many AI platform vendors are exploring how to tackle these shortcomings.
"We hope to see near-term product innovation of radiology production systems to incorporate results more easily," he said. "There are some clear signs that newer generative AI-based reporting systems and more modern architecture production systems and workflow orchestration solutions are now increasingly being built AI-ready to receive and facilitate interaction with results coming from AI sources."
Another challenge revolves around the limited functionality of current AI solutions. Wald indicated that many AI systems currently available in the U.S. are designed with limited functionality and cleared for relatively narrow use cases.
He underscored that this is expected to change over time, as it has in other parts of the world with more robust regulatory frameworks for AI.
Wald also highlighted that AI is not "push-button technology."
"AI technology is different from other FDA-cleared devices we use in radiology departments. FDA clearance does not guarantee that the technology works as demonstrated in pre-market performance testing," he explained. "Performance very much depends on similarity between training data and input imaging data -- on which the technology runs -- used locally."
Therefore, local users must verify that an AI tool works as intended and make sure that performance does not degrade over time as radiology solutions and protocols continue to evolve.
Alongside these challenges, information exchange remains an issue. Wald noted that assessing whether an AI is able to correctly classify imaging observations depends on factors outside of the imaging data or the prevalence of the condition in question.
"It also depends on clinical information and a reasonable radiologist-generated 'ground truth' where applicable, for training and/or performance assessment," he said. "Comparing AI output, radiologist output and medical information requires a degree of 'semantic interoperability' of the data."
This semantic interoperability serves to establish a shared understanding that helps determine which diagnosis or condition is represented within the data and the AI system.
"The ability of disparate systems to unambiguously match disease, anatomic, and/or location concepts across these diverse medical data sources, and to exchange individual patient-relevant content seamlessly, remains perhaps the biggest challenge today," Wald stated. "Approaches like the Open Imaging Data Model try to address some of these historically developed shortcomings."
The final challenge relates to what Wald calls "AI economics."
"Specific practice context determines whether the value proposition and return on investment for specific AI solutions are worth the expense," he said, indicating that tools like triage AI might be particularly useful for a busy emergency radiology practice, but may not be a significant value-add for a daytime outpatient imaging enterprise.
"Furthermore, the economics of AI implementation remain challenging as there is no consistent payment model and licensing fees, and implementation and maintenance costs can be substantial."