Bridging the Gap Between AI-Powered Medical Imaging and Clinical Decision-Making in Precision Oncology: Are we there yet?

Author(s):

Josesph Paxton

Key Takeaways

AI enhances radiology's role in oncology by supporting therapy choices, trial recruitment, and treatment monitoring.
Imaging biomarkers, combined with molecular data, improve predictive accuracy but require unified platforms for effective implementation.
Regulatory approvals for AI imaging tools are increasing, but clinical trust remains fragile due to the need for clear, interpretable insights.
Progress is seen in aligning imaging with care, trial design, and next-generation platforms, but full integration and real-time clinical relevance remain challenges.

Bridging the last mile to clinical actionability.

Joseph Paxton
Senior vice president and
market head of life sciences
CitiusTech

Across oncology, AI is sharpening our lens faster than ever. Radiology, which was once confined to static imaging, is now playing a far more dynamic role. It supports early therapy choices, guides trial recruitment, helps personalize care, and tracks how well treatments are working in real-time.

It classifies tumor subtypes, predicts treatment response, and surfaces risk before symptoms appear. For example, NIH researchers built an AI model that matches patients to cancer therapies by reading tumor biology and predicting drug responses.It hints at a future where imaging helps personalize treatment.¹

Yet for all the promise, the practical impact remains limited. We’ve made incredible progress in teaching machines to see, analyze, and even predict. Despite broader adoption across health systems and federal programs, more than two-thirds of AI initiatives haven’t yet broken out of the lab.² Insights are generated but not always delivered when and where decisions are made.

This brings us to the central question: how close are we to turning AI-powered medical imaging into a reliable partner in precision oncology? Progress is real, but impact depends on something more: connecting intelligence with the systems that deliver care. Some of that connection is already being built through new platforms that bring imaging, genomics, and real-world data onto a single path to action.

What’s holding us back

We’re beginning to see what’s possible when imaging biomarkers are embedded into trial workflows. A recent study used radiomic signatures from baseline CT scans to identify which patients with advanced Non-Small Cell Lung Cancer (NSCLC) were more likely to respond to immunotherapy, even before treatment began.³ That level of predictive insight changes the logic of enrollment. Instead of casting a wide net, trials can focus on the patients most likely to benefit, making each data point more meaningful and each outcome more relevant.

Predictive insight demands a multimodal view: Still, progress on one front reveals complexity on another. The value of imaging biomarkers grows when paired with molecular and clinical context, yet current systems continue to operate in isolation. A study showed that combining CT-based radiomic features with genomic data significantly improved lung cancer subtype classification compared to using either dataset alone.⁴ However, moving from insight to impact calls for platforms that can synthesize these signals within a single clinical workflow.
Regulatory greenlights haven’t resolved clinical trust: More AI imaging tools are receiving regulatory approval, but clinical trust remains fragile.⁵ Many providers are still cautious, understandably so. In critical scenarios, knowing how a decision is made is important than arriving at a conclusion. For new tools to earn a place in clinical care, they need to be built on diverse patient data and deliver insights that clinicians can interpret clearly.
Progress toward unified platforms remains uneven: We’re also seeing initial attempts to bring imaging, omics, and trial data together in decision-support platforms. But these efforts often stall due to persistent fragmentation across data standards (like DICOM, VCF, and EHR formats), limited maturity in fusion models, and inconsistent integration across workflows.⁶

The gap here is in coordination, more than discovery. And until these systems speak the same language, precision will remain out of reach.

Where imaging and care begin to align

We’re seeing early signs that AI-powered imaging is moving closer to clinical relevance. In both research and clinical spaces, imaging is being brought closer to the point of decision through stronger links with biology, trial design, and everyday care. They’re early, but they signal what could become the new normal.

Below are some spaces where that alignment is beginning to take shape:

Moving imaging insights closer to the point of care

Radiomics and pathomics are opening new pathways to predict treatment response based on patterns invisible to the human eye. In a recent study, CT-based features were able to indicate the presence of EGFR mutations and ALK fusions in lung adenocarcinoma, two biomarkers critical to therapy selection.⁷

If imaging can indicate their presence, it opens up faster, less invasive pathways to precision care. However, these insights must be explainable, tested across diverse populations, grounded in clinical reality, and usable within everyday workflows.

Collaborations between imaging scientists and oncology teams are beginning to reflect that shift. It becomes important to design tools that are predictive as well as trustworthy at the point of care. Are we there yet? Not entirely, but the intent to build clinical-grade imaging intelligence is clearly gaining ground.

Designing trials that reflect how treatment works

Participant selection remains a critical variable in trial success. A recent analysis used a CT-derived radiomic signature to identify lung cancer patients likely to benefit from concurrent chemoradiation.⁸ With a more focused cohort, trial outcomes improved and screen failures declined.

This approach is shaping a new model for trial design, where imaging strengthens eligibility criteria and helps match patients to treatment earlier in the process. Tools like mpMRI-based grading in prostate cancer and response prediction models in NSCLC reflect this shift.⁹

Yet even promising models must pass through regulatory review, be embedded in trial protocols, and align with molecular and clinical data. Imaging biomarkers can guide smarter recruitment, but only if they are recognized as decision support tools, not diagnostic accessories.

We’re seeing the scaffolding of that future. Whether it becomes standard depends on how fully trial sponsors and clinical researchers commit to this new kind of alignment.

Next-generation platforms that support continuity

Infrastructure is quietly becoming one of the most important enablers of precision care. New-generation imaging research platforms are integrating with genomic, clinical, and trial data to offer a composite view of disease, far beyond what standalone scans can provide.

These new systems help bring more consistency to diagnoses and hint at what’s possible when imaging is part of a larger, connected picture of care.

Such platforms could support longitudinal insights, such as tracking how patients respond, when disease progression diverges, and where interventions matter most. But to function at scale, they require shared standards, interoperable formats, and the kind of coordination that reaches across disciplines.

The ambition is visible. The challenge now is ensuring these platforms can serve not just researchers, but clinicians and patients who need answers in real-time.

Bridging insight and impact: The next phase for AI imaging

AI-powered imaging is already transforming disease detection, like flagging strokes, cancers, and over 50 eye conditions¹⁰ well before symptoms emerge. These capabilities are promising, but their full potential depends on how intuitively they integrate into the clinician’s workflow.

Today, imaging often exists independently of molecular insights, trial data, treatment decisions, and longitudinal patient records. Clinicians aren’t looking for more data; they need systems that bring clarity, continuity, and clinical relevance to the moment of care.

That calls for platforms shaped by day-to-day realities of oncology, such as timely decisions, unified views, and shared accountability. We’re not quite there yet. But each step toward alignment brings AI imaging closer to what it should be: a trusted partner in delivering timely, targeted cancer care.

Sources

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