Artificial intelligence is changing radiology. Across health systems, AI tools are becoming faster and more effective at identifying pulmonary nodules, lesions, and other potentially serious findings. These tools can help radiologists detect what may otherwise be missed, prioritize cases, and generate new clinical insights at scale.
But detection is only the beginning.
As Angela Adams, RN, CEO of Inflo Health, recently wrote in MedCity News, radiology is facing a last-mile problem: AI may improve detection, but follow-through still breaks. A patient may receive imaging. An AI tool may flag a concerning finding. A radiologist may recommend follow-up. The report may be finalized. And then the next step depends on a fragile chain of handoffs across clinicians, schedulers, patients, EHR inboxes, and worklists.
Too often, that chain breaks.
When follow-up does not happen, the consequences are serious. A pulmonary nodule may go unmonitored. A recommended exam may never be scheduled. A referral may sit unresolved. The patient’s risk increases, and the health system is left exposed to clinical, operational, and legal consequences.
That is why the next era of radiology AI cannot stop at finding more. It must focus on finishing better.
Finding It Is Not the Same as Fixing It
Radiology is entering a paradoxical phase. AI is becoming more capable at detecting clinically meaningful findings, but many health systems still struggle with what happens next.
The issue is not simply whether a finding appears in a report. The issue is whether that finding results in appropriate, timely, documented care.
A follow-up recommendation can trigger a long downstream process: notifying the right provider, clarifying clinical responsibility, placing the order, securing prior authorization, scheduling the exam, reaching the patient, managing reminders, documenting completion, and reconciling the outcome back to the original finding.
That work is complex. It crosses departments. It often crosses systems. And in many organizations, it is still managed through manual processes: spreadsheets, inboxes, phone calls, recurring huddles, and worklists that depend on already-stretched teams.
This is where AI can unintentionally create more work. A tool may identify more actionable findings, but if those findings are not tied to a reliable follow-up workflow, the result is a growing backlog of tasks. A dashboard may show risk, but it does not schedule the patient. A worklist may surface a finding, but it does not guarantee ownership. An alert may notify a clinician, but it does not confirm that care was completed.
In other words, detection without orchestration can amplify the very gaps health systems are trying to close.
The Diagnostic Alert Trap
Many radiology AI tools are designed around the front end of care. They detect, prioritize, or summarize. These capabilities are valuable, but they are not the same as a closed-loop follow-up program.
The “diagnostic alert trap” happens when a health system assumes that identifying a finding is enough to solve the follow-up problem. In practice, alerts and worklists often create parallel workflows that require human teams to reconcile what the AI found with what happened in the EHR.
That creates operational drag. Staff must determine who owns the next step. Clinicians must decide whether the recommendation is still appropriate. Schedulers must connect with patients. Leaders must monitor completion. Quality teams must audit whether the loop was actually closed.
Without a defined closure event, every open recommendation becomes another unresolved task. Over time, the backlog grows. Teams spend more energy triaging and re-triaging than moving patients to the next step.
Radiology AI will not reach its full value if it leaves health systems with more things to chase.
What Closed-Loop Follow-Up Actually Requires
Closed-loop follow-up means there is a measurable end state. An actionable finding should not simply generate an alert. It should start a pathway that ends in one of three outcomes:
- The recommended follow-up exam is completed.
- The referral or next step is completed.
- The recommendation is clinically resolved with a documented rationale.
That end state matters because it turns follow-up from an informal expectation into an auditable workflow. It allows health systems to know which patients are on track, which cases are overdue, which handoffs are failing, and where additional support is needed.
A reliable closed-loop program requires more than a model. It requires orchestration.
Orchestration connects people, processes, and systems so that follow-up does not depend on memory, inbox management, or heroic manual effort. It helps ensure the right people see the right information at the right time. It separates routine logistics from moments that require clinical judgment. It supports risk-based triage, so teams can focus attention on the patients and findings that need it most.
This distinction is especially important as imaging volumes continue to rise. Health systems do not need every ambiguous phrase to become another task. They need specificity: what is actionable, why it matters, when follow-up is due, who owns it, and what counts as completed.
The Next Wave of Radiology AI Value
The next wave of radiology AI value will not come from detection alone. It will come from the ability to operationalize the finish.
As highlighted in the MedCity News article, health systems should focus on several practical moves: define the closure event, assign ownership across handoffs, demand integration that removes work rather than creating new queues, and treat governance as a patient safety function.
That last point is critical. AI governance is not just paperwork. It is how health systems determine where AI outputs appear, who sees them, how they are labeled, and how they are acted upon. Poor governance leads to workarounds, duplicate reviews, and mistrust. Strong governance creates clarity, accountability, and safer workflows.
Radiology will continue to be a proving ground for clinical AI. But the organizations that lead this next phase will be the ones that stop measuring success only by what AI finds. They will measure success by whether patients receive the care those findings require.
Detection matters. But in healthcare, the finish matters more.
Read Angela Adams’ full MedCity News article here: The Last Mile Problem in AI Radiology: Detection Improves, Follow-Through Breaks.
