After years of flat or falling payment rates, radiology providers are being asked to do more with less. Medicare cuts continue to squeeze margins, and inflation-driven cost hikes in labor and supplies further strain budgets. At the same time, workforce shortages have left many radiology departments shorthanded and clinicians overextended. Meanwhile, the traditional follow-up worklist-centric model—essentially a growing to-do list of patients needing outreach, navigation, and scheduling—is showing its cracks.
Agentic AI is flipping that script. Instead of merely tracking tasks for humans to complete, these AI-driven agents actually perform the tasks, handling routine follow-up tasks autonomously so that people don’t have to. For example, an agent can identify all patients due for imaging follow-ups, prioritize them by clinical urgency, and initiate the next steps—from contacting patients to coordinating paperwork—without dumping extra tasks on the clinical team. The goal is to take work off human plates, not add to it. With hospital C-suites bracing for budget cuts and tighter headcount, technologies that remove labor-intensive steps (while ensuring no patient falls through the cracks) are rapidly becoming essential.
Contextual Intelligence for Radiologists’ Reads
One promising application of agentic AI is closing the long-standing radiology “context gap” created by siloed systems and disparate data. Radiologists are often asked to interpret studies with minimal background—sometimes just a one-line reason for an exam (“back pain,” etc.). Lacking clinical context, radiologists may hedge their reports or give vague recommendations. It’s not uncommon to see a note like “If symptoms persist, consider an MRI,” leaving the referring physician unsure what to do next. These ambiguities stem from radiologists being disconnected from patient data; in many cases, they don’t have easy access to the full electronic health record (especially teleradiologists reading remotely).
Agentic AI can bridge this gap. AI agents are capable of automatically pulling rich clinical context from the EHR for the radiologist. Before a study is read, the agent could scour the patient’s records for relevant history, prior exams, symptoms, and risk factors, then present a concise summary. Armed with this information, the radiologist can tailor their impressions and recommendations with far greater specificity. They could, for example, confidently recommend a contrast MRI of the lumbar spine in 3 months, because the AI offered up the clinically relevant details about the patient’s previous interventions and current condition.
Automating the Follow-Up Workflow End-to-End
Crucially, an agent doesn’t stop at enriching the read, it can also help execute the next steps. Beyond assisting radiologists, agentic AI is tackling the tedious follow-up tasks that typically burden care navigators and staff. Traditionally, after a radiologist flags an abnormal finding or recommends a follow-up, a labyrinthine process begins: someone must log the recommendation, communicate the finding to the referring provider, find the patient’s contact info, reach out to explain the next steps, secure a pre-authorization, schedule the appointment, and perhaps loop in specialists.
Agentic AI can serve as an automated follow-up coordinator that makes sense of the chaos. Upon detecting a follow-up recommendation in a report, an AI agent can immediately extract the key details and kick off a cascade of actions. One agent might verify if a new order and pre-authorization are needed and, if so, prepare the submission with the payer-specific information. Another agent could place a courtesy call or text to the patient to explain the recommendation and begin scheduling. If the patient has questions, a conversational AI can respond in plain language and can escalate to a human when a patient has clinical questions. These patient-facing agents can reduce unnecessary back-and-forth, lower administrative drag, and unlock human time and energy toward patient care.
By automating such downstream tasks, health systems create an “automated safety net” that catches each recommendation and carries it through to completion. This dramatically improves reliability: every patient who needs a follow-up is identified, informed, and guided through the process, without cases slipping through the cracks because processes are broken and people are overtaxed. Fewer patients are lost to follow-up, meaning better outcomes and fewer liability risks for providers. It also means more recaptured revenue—instead of losing patients to leakage or failing to convert orders, the system closes the loop on care that has been recommended.
Opportunistic Screening Becomes Routine
Another area ripe for agentic AI is opportunistic screening, which turns incidental findings into proactive care. Imaging studies often contain more information than their primary target. A CT scan ordered for back pain, for example, might incidentally reveal coronary artery calcifications or low bone density. There is a history of caution around highlighting these “additional” findings; the concern is that by noting an incidental risk (like possible coronary disease) without a guaranteed follow-up, additional liability could be created if nothing is done about it
Agentic AI offers a path to navigating these incidental findings and ensuring follow-up. If a radiologist spots a noteworthy incidental finding, an agent can swing into action to ensure it’s acted upon. For instance, imagine a CT shows moderate coronary artery calcification (CAC) in a patient. An AI agent can pull additional context from the record—are they on a statin? Do they have hypertension or other cardiac risk factors?—and compile a brief summary. Based on this, the radiologist can review the findings and generate a tailored recommendation. The agent can pick up from there to automatically notify the patient’s primary care physician or cardiology department, and even assist with referral scheduling. All of this can happen in the background, so that the incidental finding becomes an actionable care event.
The payoff is healthier patients and a healthier bottom line: catching a brewing cardiac issue might prevent a heart attack (a win in value-based care terms) while also generating new downstream revenue for the health system (a win in fee-for-service terms).
Navigating the AI Hype Responsibly
As agentic AI gains buzz, healthcare leaders must navigate the hype cycle wisely. The term “agentic AI” itself has become a bit of a buzzword, often without a clear definition. It’s important to cut through the noise. On one side, there are the incumbent legacy technology vendors—large legacy EHR or radiology IT companies that move at a slower pace and offer little beyond the status quo. On the other side, there’s a swarm of new AI startups, some of which promise the moon but have unproven solutions (the healthcare equivalent of “vaporware”). Neither extreme is the right choice.
Bottom line: Radiology is poised for its next efficiency leap, and it’s coming from intelligent automation that moves beyond the worklist. Agentic AI promises a future where radiology departments can handle increasing volumes and complexity without proportional increases in workload—a future where every follow-up is captured, every patient is informed, and clinicians can focus on care rather than paperwork. The pressures facing health systems aren’t letting up, but with the strategic use of AI agents as an “automated safety net,” radiology leaders can boost productivity, improve patient outcomes, and thrive even in a do-more-with-less era.
About Angela Adams
Angela Adams, RN, has been advancing the industry by applying AI to improve healthcare outcomes for over a decade. Angela started her career as a critical care medicine nurse at Duke University Medical Center. During her time in the hospital setting, Angela became increasingly frustrated with the inefficiencies in patient care. Driven to make a broader impact, Angela looked to the emerging healthcare AI segment for solutions that would allow her to help patients as well as assist clinicians to become more effective and efficient in solving complex medical issues. She helped advance AI adoption and overcome skepticism at companies like Jvion (acquired by Lightbeam Health Solutions), where she applied deep machine learning to lower nosocomial event rates and prevent patient deterioration. She went on to create her most recent solution at Inflo Health, where she focuses on missed follow-up radiology appointments.
