Karim Karti, CEO of RapidAI, joined Healthcare IT Today to talk about why radiology AI platforms need to be more adaptable to the realities of modern healthcare. Radiologist shortages, staff burnout, cyber incidents, fragile connectivity, and tight budgets all shape how imaging technology is used day to day, often in ways product roadmaps did not originally anticipate.
Key Takeaways
- Cloud architecture is helpful to radiologists for many reasons. We must assume downtime will happen and design for it; RapidAI’s ability to toggle seamlessly from cloud to on-prem is a direct response to real cyber events.
- The goal of AI in radiology is prediction, not just productivity. Workflow efficiency is only the stepping stone; the real strategic shift is toward predictive imaging that supports earlier intervention and prevention.
Why Resilient Cloud Architecture Matters Beyond Cybersecurity
Cloud adoption in healthcare is often framed around efficiency and scale, while downtime planning gets treated as an edge case. RapidAI took a different approach with its Edge Cloud infrastructure, designed to operate in the cloud while also supporting on-prem when needed, without interrupting clinical workflows.
That flexibility acknowledges the operating reality that many other vendors miss: healthcare organizations experience many forms of disruption, from cyber incidents to connectivity gaps, and patient care does not pause for any of them.
Karti explained that the approach was driven directly by customer experience and feedback, especially in high-stakes settings like stroke care.
“We built it based on customer needs and the requests we received. In the case where customers are experiencing a cyberattack or need to disconnect from the cloud, they still need our application, especially for acute patients.”
That capability has already been used in real-world situations.
“We already have many instances where actually customers had to use this capability. We were one of the few applications actually running the hospitals and allowing patients to be treated.”
As healthcare IT infrastructure becomes more complex and interconnected, interruptions are increasingly part of the operating environment. Systems that can continue functioning without full connectivity are no longer just safeguards. They are part of how care gets delivered reliably under less-than-ideal conditions.
How Predictive AI Is Becoming the Real Endpoint for Radiology
Efficiency gains often dominate AI discussions in radiology, but Karti pointed to a longer horizon. As imaging volumes rise and workforce constraints persist, productivity improvements alone are not enough. The greater opportunity lies in using AI to help radiologists move from reactive interpretation toward predictive insight.
“Ultimately where all this is going is helping them get into more predictive capabilities, because that’s what patients are looking for, that’s what physicians are looking for.”
Early AI adoption has focused on removing repetitive tasks and accelerating reads. As the technology matures and trust grows, those gains create space for more advanced use cases. Earlier detection, better tracking over time, and more informed intervention all become possible when radiologists can focus less on volume and more on what comes next.
What Infrastructure Choices Reveal About the Future of Care Delivery
RapidAI’s approach highlights a factor that will become an important factor in how healthcare technology is evaluated. Cloud solutions increasingly need to account for continuity, not just convenience. At the same time, AI platforms are being measured by their ability to support long-term clinical insight, not just short-term efficiency.
Together, those priorities reflect where radiology is heading: toward systems designed for real-world conditions, and toward care models that emphasize anticipation over reaction.
Learn more about RapidAI at https://www.rapidai.com/
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