What Is Agentic AI (and Why It’s Different)?
Imagine an AI system that not just answers your questions but proactively makes decisions and takes action – smartly planning tasks and learning as it goes. That’s the idea behind agentic AI. Unlike traditional AI assistants that react to commands (think of your voice assistant setting a timer when asked), agentic AI operates autonomously. It can work intelligently and independently, pursuing goals through multiple steps and reasoning without constant human prompts - “What Is Agentic AI, and How Will It Change Work?” (HBR, Generative AI, December 2024).
In other words, this is an AI with agency: it is proactive, goal-driven, and capable of adapting and improving its strategies over time. An example from the HBR article describes agentic AI as a system that can plan an overseas trip from start to finish or dynamically manage a supply chain—tasks that go far beyond those of a simple question-and-answer assistant.
This evolution means we’re moving from AI as a tool that follows instructions to AI as a collaborator that can figure out the instructions independently. With enhanced reasoning and execution abilities, agentic AI promises to transform how we work with machines. But what does that mean for something as critical as medical imaging?
Transforming How Doctors Analyze Images
Agentic AI is poised to revolutionize medical imaging – the X-rays, ultrasound, CT scans, MRIs, and more that doctors rely on for diagnoses. Today’s AI tools are already incredibly good at scanning images and spotting patterns. They can sift through scans with remarkable precision, often catching details that a human might miss. According to the American College of Radiology, AI algorithms can enhance the detection of tumors or fractures on medical images, leading to earlier interventions and better patient outcomes. That means finding problems sooner and starting treatment faster, which can be lifesaving for patients.
Now, add agentic capabilities to this. Instead of pinpointing a suspicious shadow on an X-ray, an agentic AI system might pull up the patient’s past scans for comparison, recommend a follow-up MRI, and even prioritize the scheduling of that exam – all on its own. It acts like an intelligent assistant with initiative, identifying an issue and helping drive the next steps in care. While doctors maintain control, they benefit from insights and data that the AI proactively curates.
Consider a few examples of how this works in practice:
Detecting Illness in Seconds: In radiology, AI agents can swiftly scan an image (say, a lung CT or a mammogram) and highlight suspicious lesions or tumors within seconds, acting as a reliable second set of eyes for the radiologist. Using an AI agent saves time and adds confidence – if both the AI and the doctor spot the same anomaly, there’s a better chance it needs attention. And if the AI catches something the human missed, it can prevent a potential oversight.
Surgical Planning Made Easier: Surgeons often spend hours reviewing MRI or CT images before a complex surgery. Agentic AI can shorten this process by automatically analyzing the scans and generating a 3D model of the patient’s anatomy, pinpointing critical structures like blood vessels and tumors. For instance, AI models can automatically segment (outline) organs and tissues on CT and MRI images. This task used to take radiologists a lot of time. By doing the heavy lifting – mapping out the anatomy and even suggesting optimal pathways for surgery – the AI frees up surgeons to focus on strategy. One recent AI tool in Switzerland, called TotalSegmentator, could segment dozens of anatomic structures on MRI, reducing workload and human error while providing consistent, detailed results.
Learning and Personalizing Care: Perhaps one of the most exciting aspects is how these AI systems learn and personalize over time. An agentic AI doesn’t just perform a task and stop; it can incorporate feedback and new information to get better with each case. If a doctor corrects a system’s suggestion, the AI can treat it as a learning moment. Over time, it adapts its algorithms – much like a doctor gaining experience. In oncology, for example, an AI agent could correlate imaging findings with genetic data from a biopsy to recommend personalized treatments tailored to a patient’s specific cancer markers. As more patient outcomes are fed back into the system, the AI agent refines its knowledge of what treatments work best for which types of patients. This kind of continuous, adaptive learning means the AI’s performance doesn’t plateau; it keeps improving, offering clinicians more nuanced support each month.
A Collaborative Future in Healthcare
What does all this mean for medical professionals? Crucially, agentic AI in medical imaging is about empowering doctors, not replacing them. These AI agents are tireless partners – combing through data at lightning speed, handling routine but time-consuming tasks, and presenting distilled insights to human experts. By automating the grunt work, AI agents allow clinicians to devote more attention to the human side of medicine: confirming diagnoses, planning treatments, and talking with patients. As one radiology researcher noted, automation can “reduce a radiologist’s workload, minimize errors, and provide more consistent results.”
In practice, this might mean a doctor spends less time clicking through dozens of images and more time explaining a diagnosis and treatment options to a worried patient.
Notably, the partnership is symbiotic. The AI gets smarter from physicians' corrections and guidance, and the physicians benefit from an ever-watchful assistant that never gets tired. It’s a feedback loop of improvement. Of course, like any new technology, agentic AI systems must be developed and used carefully – we need to ensure they are accurate, unbiased, and secure. But when built and used responsibly, they can be a powerful force multiplier in healthcare.
Ultimately, agentic AI is accelerating the use of AI in medical imaging by making these tools more intuitive and more integrated into clinical workflows. We’re entering an era where your radiologist might routinely consult an AI agent for a second opinion on a scan, or a surgeon might lean on AI to help map out a complex operation. The tone is collaborative: it’s about an AI partner that extends what doctors can do. As necessary, this technology is accessible and understandable – you don’t need to be a tech expert to benefit from it, either as a doctor or a patient. It’s a reliable system working in the background to ensure no critical information slips through the cracks.
Bottom line: Agentic AI is giving medical professionals a new ally. Handling heavy data-crunching and providing intelligent support lets humans do what they do best – care for patients – with greater insight and confidence. The future of medical imaging isn’t man or machine; it’s the powerful combination of both working together.