Cancer care is in the midst of a profound transformation.
For decades, oncology operated on standardized treatment protocols built around population averages. While this approach brought consistency and scalability, it often overlooked individual biology, unique risk factors, and variability in patient outcomes.
Today, precision oncology is rewriting that model. Instead of treating the disease in broad strokes, clinicians are increasingly focusing on delivering the right intervention to the right patient at the right time. At the heart of this evolution is a powerful new capability: AI-enabled biomarkers—tools that are helping detect cancer risk earlier and enabling meaningful stage shifts in diagnosis.
In This Content
The Move Toward Precision Oncology
Precision oncology shifts care from a reactive model to a proactive, data-driven one. It integrates biological insights, clinical data, and risk profiling to guide individualized decisions about diagnostics, surveillance, and treatment.
A simple but crucial truth underpins this shift: timing changes outcomes.
Identifying which patients need urgent escalation versus those who can be safely monitored influences survival, resource allocation, and patient experience. Increasingly, this risk assessment begins earlier—sometimes even during routine imaging performed for unrelated reasons.
Incidental findings, once considered peripheral, are now becoming powerful triggers for earlier diagnosis and intervention. This upstream movement creates opportunities to detect disease at earlier stages, where treatment is often more effective and less invasive.
Understanding AI-Enabled Biomarkers
Traditional biomarkers typically focus on a single molecular signal—such as a blood test or genetic mutation. AI-enabled biomarkers, however, represent a new generation of clinical intelligence.
These tools use machine learning algorithms to analyze vast amounts of routine clinical data, especially medical imaging. Instead of looking for one marker, they detect complex patterns across thousands of subtle image features simultaneously.
This approach brings three major advantages:
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Scale – AI can evaluate large volumes of data consistently.
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Precision – It identifies patterns too subtle for the human eye.
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Timeliness – It provides risk insights earlier in the care journey.
Rather than replacing clinicians, AI augments their ability to stratify risk and make informed decisions sooner.
Moving Risk Stratification Upstream
One of the most significant changes AI is driving is the repositioning of risk assessment.
Historically, stratification began after a diagnosis was already confirmed. Today, AI tools are enabling risk evaluation at the point of first imaging—or even during incidental scans performed for other reasons.
This upstream shift delivers tangible clinical and operational benefits:
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Faster diagnostic escalation for high-risk patients
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More efficient referral pathways
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Reduced delays in initiating treatment
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Better allocation of imaging and specialist resources
Ultimately, these improvements contribute to earlier detection and improved stage distribution at diagnosis.
A Real-World Example: Qure.ai’s Lung Nodule Malignancy Score
A practical illustration of this innovation is the Lung Nodule Malignancy Score (LNMS) developed by Qure.ai.
LNMS is an AI-derived imaging biomarker that evaluates the malignancy risk of lung nodules identified on chest X-rays. By analyzing radiographic patterns across multiple dimensions, the system differentiates between higher-risk and lower-risk nodules early in the diagnostic pathway.
The clinical impact is meaningful:
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Physicians gain immediate risk insights at the imaging stage
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High-risk patients can be fast-tracked for further testing
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Lower-risk patients can avoid unnecessary invasive procedures
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Existing imaging workflows remain unchanged
By embedding intelligence into routine radiology, the likelihood of detecting lung cancer at an earlier stage increases—without adding complexity to frontline care.
Transforming Clinical Decision-Making
The power of AI-derived biomarkers lies not just in detection, but in action.
These insights influence downstream decisions such as:
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Whether to escalate to advanced imaging or biopsy
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How urgently to refer to oncology specialists
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How to sequence treatment options
By integrating AI risk scores directly into clinical workflows, healthcare teams can transition from reactive responses to proactive planning. This shift strengthens precision oncology by aligning decision-making with real-time, data-driven insight.
Expanding Impact: Research and Clinical Trials
The implications extend beyond direct patient care.
AI-enabled biomarkers are also reshaping oncology research and drug development. By improving patient selection and eligibility matching, these tools can:
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Accelerate clinical trial enrollment
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Improve stratification in study populations
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Enhance predictability of treatment outcomes
Smarter selection means trials can move faster and therapies can reach patients more efficiently.
Looking Ahead
AI-derived imaging biomarkers are no longer theoretical innovations. They are rapidly becoming foundational infrastructure within modern oncology.
By moving risk stratification earlier in the care pathway, informing treatment readiness, and translating insight into decisive action, these tools strengthen every layer of cancer management.
As the technology matures, AI-enabled biomarkers will increasingly complement molecular diagnostics and precision medicine strategies. The result is a more intelligent, responsive, and patient-centered oncology ecosystem—one that prioritizes earlier intervention, optimized care pathways, and improved outcomes worldwide.
The future of cancer care may not rest on a single breakthrough—but on smarter systems that help us see risk sooner and act with greater confidence.