First-of-its-kind framework monitors real-time performance of radiology AI tools
A new artificial intelligence (AI) solution could be a game changer for companies seeking to track the impact of AI tools that have been integrated into their workflows.
This week, radiology informatics leader Bialogics Analytics announced the rollout of AI Quality Framework (AIQ), a new evidence-driven framework for monitoring the performance of radiology tools. The first-of-its-kind framework offers organizations real-time, detailed assessments of how algorithms implemented into workflows are impacting efficiency and outcomes.
The product’s launch comes as data has signaled that more than half of healthcare organizations either already have or are planning to implement some sort of AI into their clinical practices. With over 1,000 FDA-approved AI algorithms targeting imaging, it has become clear many see the field of radiology as a place where AI can make the most meaningful difference in clinical workflows and patient care.
However, AI algorithms must continuously be monitored, updated and validated. This has been highlighted as a point of contention among organizations looking to invest in AI solutions. Bialogics President Jeff Vachon believes AIQ can help leaders address these concerns.
“With AIQ, we’re shifting AI evaluation from intuition and isolated testing to a continuous, data-backed strategy that aligns with the clinical rigor that radiology demands,” Vachon said in an announcement. “This is evidence-driven AI performance in action, where real-world metrics directly empower clinical practice.”
AIQ offers monitoring of key performance indicators, such as concordance between algorithms and readers, sensitivity, specificity, positive predictive value, negative predictive value, accuracy and enhanced detection rate. It tracks report turnaround times and individual radiologists’ workloads, while also surveilling for signs of bias and AI drift. The framework incorporates each of the aforementioned to calculate an AI score that offers organizations detailed insight into the clinical performance of the products they have implemented.
Vachon suggests that by increasing transparency, the framework will improve provider and patient trust in AI.
“Healthcare leaders need more than just theoretical models, they need proof,” Vachon said. “AIQ gives them the clinical evidence, operational insights, and real-time data needed to confidently evaluate and implement AI technologies at scale.”
Learn more about AIQ here.
In addition to her background in journalism, Hannah also has patient-facing experience in clinical settings, having spent more than 12 years working as a registered rad tech. She began covering the medical imaging industry for Innovate Healthcare in 2021.