AI’s diagnostic power in Radiology is not limited to screening

AI doesn’t just help, it transforms; reduces error rates, sharpens radiologist focus, and improves case throughput.

By Kalyan Sivasailam

Most people still talk about AI in healthcare as if it’s a glorified metal detector: capable of flagging potential danger but incapable of deeper insight. The word they use, almost reflexively, is screening. It’s a safe word. A familiar word. But it’s also a deeply limiting one.

Screening implies AI is only useful on the periphery—catching what’s obvious, triaging what’s low-risk, and staying out of the way. That might have made sense a decade ago, when AI needed to earn its stripes. But in 2025, this (mis) understanding of what AI can do in healthcare is actively holding the field back.

AI is no longer just scanning for red flags. It’s being built to reason, report, and refine. If we keep calling it a screening tool, we risk anchoring the entire field to its least ambitious use case.

The narrative around AI as a screening tool persists not due to any technical limitation, but due to a mindset limitation. Over the last few years, I’ve seen AI shift from being a sidekick to becoming a serious contributor in diagnostic workflows. But the language hasn’t caught up. Screening suggests something basic, blunt, and optional. Today’s systems are none of those. They’re nuanced, collaborative, and increasingly capable of sharing diagnostic responsibility.

From Flagging to Finishing

Sure, early-stage AI needed guardrails. Flagging a single disease in chest X-rays or identifying diabetic retinopathy was a low-risk, high-volume entry point. It helped build trust and satisfy regulatory requirements. But radiology departments are called Radiodiagnosis departments, not Radioscreening departments—and the tools are vastly different.

Ask any practising radiologist what they actually need—and I do, often—and you’ll hear the same thing: the challenge isn’t catching what’s obviously normal or clearly abnormal. It’s the messy middle. The maybes. The cases that don’t shout, but whisper—those that require clinical intuition, pattern recognition, speed, and stamina. That’s where the real cognitive load sits.

Radiologists don’t want a tool that taps them on the shoulder after the fact. They want one that rolls up its sleeves and works beside them in real time. Not a watcher, not an auditor—but a co-worker.

The human brain is brilliant, but it’s also human. Interpretive errors occur in 3–5% of all imaging studies. Even when well-rested and focused, two qualified radiologists can disagree on the same scan nearly a third of the time. A single reader may reverse their own conclusion one in five times. That’s not just variability—it’s vulnerability. And it’s precisely where embedded, thinking AI can—and should—step in.

Embedded, Evolving, Essential

When I say embedded, I don’t mean AI that waits patiently outside the workflow. I mean AI that works inside it—reading, generating, escalating, and learning in real time. I’ve seen this play out first-hand. In one implementation, our models now autonomously close over 15% of incoming scans—mostly normal chest X-rays and routine extremities—with zero human edits. Edge cases are flagged, escalated, and adjudicated within minutes, not hours. The loop closes fast, the learning compounds weekly, and the model keeps getting smarter.

This kind of AI doesn’t just help—it transforms. It reduces error rates, sharpens radiologist focus, and improves case throughput. It doesn’t merely take work off radiologists’ plates—it gives them space to focus on the cases that actually require human judgement. And it does all this while reducing cost per diagnosis—at scale.

Despite all this, the term screening stubbornly lingers in investor decks, regulatory filings, and procurement conversations. And it’s holding us back. Language shapes expectations. Expectations shape investment. And underestimation invites underinvestment. If AI is always seen as a front-line filter, it will never be trusted with second-line judgement. If hospitals are still buying AI for screening in 2025, they’re solving yesterday’s problem at tomorrow’s cost.

Let’s Build for What’s Coming

We need a new vocabulary for this next phase. Call it a co-pilot. A clinical intelligence layer. A Clinical Language Model. A diagnostic engine, even. Just don’t call it a screener. That word belongs to a version of AI with limited function. The version we’re building now learns on the job, adapts to ambiguity, and shows up every day—for every scan, for every disease.

I’m not suggesting screening didn’t play a role. It paved the road. But now we need systems that finish the job. The ones that not only highlight risk but draft reports. The ones that don’t just flag shadows, but offer explanations. That’s what real diagnostic AI looks like.

Let’s stop calling AI what it used to be. Let’s call it what it’s becoming: a partner in diagnosis, a real-time decision tool, and a new kind of medical intelligence built to scale.

(Kalyan Sivasailam, Founder & CEO, 5C Network.)

Views are personal and do not represent the stand of this publication.