The Cancer Center’s Secret Weapon: Closing the Loop on “Suspicious” Findings

The worst sentence an oncology leader can hear is: “We had the images, but we missed the cancer.”

That scenario is often the end result of something much more mundane: a suspicious or ambiguous radiology finding that never led to follow‑up care. A lesion described as concerning, a nodule that’s suspicious for malignancy, or an incidental mass mentioned halfway down a report—each one is an opportunity for early detection that can quietly evaporate in the chaos of day‑to‑day care.

The phenomenon is more than an anomaly. Studies estimate up to 10% of radiology reports include some follow-up recommendation, yet roughly half of those recommended exams are never performed.

Most healthcare leaders don’t need a long primer on this problem. They see the downstream impact in delayed diagnoses, avoidable harm, and litigation. What’s changing now is the set of tools available to tackle it.

A new class of AI‑driven workflows combining tailored large language models (LLMs)with clinical context and intelligent task automation is giving cancer centers and health systems a way to systematically close the loop on incidental and suspicious findings, rather than relying on memory, manual tracking, and good intentions.

AI as a Safety Net

Historically, follow‑up on radiology findings has depended heavily on individual clinicians: the radiologist who remembers to add explicit instructions, the referring provider who reads every line of a report, the nurse navigator who notices that a recommended CT was never scheduled.

AI can change that equation by acting as a safety net underneath that human workflow.

Modern LLM tools, trained on radiology reports, can scan every report in real time and look for high‑risk language or specific diagnostic phrases associated with cancer. They don’t get tired, distracted, or buried under inbox volume. When these tools detect a recommendation for additional imaging, they flag the report for follow‑up.

All of this is wonderful, or course, until already-strapped care coordinators are left staring down  a bigger and bigger pile of follow-ups that now need to be coordinated and executed. AI solves one problem, yet creates another.

So the second half of the equation—the workflow—is where AI and automation can create true transformation, solving the problem of its own making. Rather than generating a static list that someone must remember to review, AI‑enabled systems can:

• Create or confirm a follow‑up order (e.g., additional imaging, biopsy, specialist consult).
• Notify the appropriate clinician within the EHR, in their existing workflow.
• Trigger outreach to the patient to explain next steps and support scheduling.
• Track each case until a definitive outcome is documented, escalating when needed.

Tackling a System‑Level Problem

In hospitals and integrated delivery networks, patients often move between settings: an ED visit here, an outpatient MRI there, primary care in a separate clinic. Each transition introduces another opportunity for handoffs to break down. A radiology report might be finalized at Hospital A, routed to a physician at Clinic B, and never connected to the oncology team at Center C.

AI‑driven follow‑up systems give organizations a way to treat this as a system problem, not a personality problem. Instead of hoping each clinician catches every important line in every report, health systems can monitor suspicious findings centrally across all sites, service lines, and providers.

That means:

• A lung nodule described on a weekend CT doesn’t depend on a single busy hospitalist to notice and act.
• An incidental breast lesion identified in an ED scan doesn’t get lost when the patient is discharged.
• A vague, concerning mass doesn’t sit in a report that no one opens again.

In an environment where staffing is tight and volumes are high, having this automated backstop is increasingly the difference between hoping care is coordinated and knowing it is.

What Success Looks Like

When these systems work well, the impact shows up in several ways:

1. Earlier Stage Detection: Findings that would have been overlooked, especially small, ambiguous lesions, are more likely to prompt timely workups. Even if only a small fraction of flagged cases turn out to be cancer, shifting those diagnoses from late‑stage to early‑stage has dramatic implications for survival, quality of life, and cost of care.
   
2. Reduced Leakage and Delays: Automated reminders and tracking reduce the number of follow‑ups that are ordered but never scheduled, or ordered in one setting and forgotten in another. Patients are less likely to be lost between imaging, diagnosis, and treatment.
   
3. Lower Risk and Fewer “Never Events”: While no system can eliminate every missed diagnosis, having a documented, auditable process for detecting and acting on findings significantly reduces exposure. It is easier to show that the organization had a robust mechanism in place and that findings were communicated, documented, and escalated.
   
4. Stronger Relationships with Referring Clinicians: When radiology is consistently closing the loop on its recommendations, referring providers experience fewer unpleasant surprises and more support. Suspicious findings become points of collaboration rather than loose ends.

Implementation Realities: Guardrails Required

Of course, no AI safety net is perfect out of the box. Healthcare leaders weighing these tools should go in with eyes wide open about the trade‑offs and practical considerations.

• Clinical governance: These systems work best when governed by a multidisciplinary group: radiology, oncology, primary care, IT, quality, and risk. That group should define what constitutes a “suspicious” finding worth triggering, what the standard follow‑up actions should be, and how escalation works if nothing happens.
• Integration, not another silo: If an AI follow‑up tool lives in a separate dashboard that staff have to remember to check, adoption will struggle. The most effective deployments embed alerts and tasks directly into existing workflows.
• Transparency and communication: Front‑line clinicians need to understand what the AI is doing and what it is not doing. It should be framed as an assistant that catches potential gaps, not as a black box making decisions on its own. Clear expectations about who ultimately owns follow‑up decisions are essential.

From Vulnerability to Advantage

Suspicious findings will always be part of radiology—ambiguity is inherent in imaging. The question for cancer centers and health systems is whether those findings become a persistent vulnerability or a managed, monitored process.

AI‑driven workflows aren’t a silver bullet, but they do provide something that has been hard to achieve with manual approaches alone:

• Consistency across sites and clinicians.
• Visibility into where follow‑ups stand at any moment.
• The ability to learn and improve the process over time.

For oncology leaders, that translates into more patients entering treatment while their disease is still curable, fewer “we had the images, but…” conversations, and a stronger foundation for value‑based, safety‑driven care.

For health system executives, it offers a path to address one of the most stubborn sources of avoidable harm without adding yet another burden to already taxed teams.

In a world where a single overlooked phrase in a radiology report can change the course of a patient’s life, building a safety net under those suspicious findings may be one of the most high‑leverage investments a cancer program—or any organization that relies on imaging—can make.