For the first time, we have tools capable of perceiving the world, interpreting it and autonomously taking steps to improve outcomes.
We already have AI, so what’s new? What makes agentic AI different from the systems we’ve been talking about for years? The answer comes down to one word: action. Large language models and generative AI are designed to reason and explain.
They process data, identify patterns and describe their own reasoning. But agentic AI goes further; it perceives, reasons and takes autonomous action. For the first time, we have tools capable of perceiving the world, interpreting it and autonomously taking steps to improve outcomes.Agentic AI adds a new layer to the clinical ecosystem, integrating into workflows and extending our capabilities. We’re currently seeing it applied in adaptive triage, workflow orchestration and patient-facing virtual care.AI agents can open a patient’s electronic medical record, scan their lab results and flag potential problems. When the white blood cell count is high, it recognizes this as a sign of infection and immediately sends an alert to your care team. If pneumonia shows up on your chest X-ray, it triages you to the correct specialist. These agents help separate sick from non-sick patients quickly and accurately, a difference that can be lifesaving.With multiple doctors involved, communication often stalls. Workflow agents coordinate across teams, synthesize key data and schedule follow-ups in the right order. It reduces confusion, closes gaps in communication and provides the entire care team with a clearer view of the patient’s care journey.These are the systems that interface directly with patients and provide recommendations, coaching and follow-up support. In my own specialty of hepatology, an agent can detect liver disease, advise you of lifestyle changes, connect you to programs and update your physician. In short, itWhere Agents Are Already ActingAgents in radiology are already reading imaging reports to identify urgent findings and flag them for immediate review. If an image suggests a possible tumor, the agent can send an alert to the on-call radiologist in minutes and accelerate the time-sensitive decisions that affect patient outcomes. In pathology, agents help narrow the pathologist’s attention to the most relevant area on a slide where the issue is present. Rather than reading every millimeter of tissue, the pathologist can immediately zoom in on the area of interest, reducing turnaround time and thereby increasing diagnostic precision. And in population health, this new model of proactive medicine is already starting to reshape what’s possible. In my own work, I’ve built an agent called FibroGPT, designed to predict a patient’s risk of advanced liver disease, including cirrhosis and fibrosis. But this tool doesn’t stop at prediction; it acts. When it identifies a high-risk patient, it checks current clinical guidelines and automatically recommends next steps, which could include a referral to a specialist, a confirmatory imaging test or even a liver biopsy. One simple agent, developed by one physician, acting on behalf of thousands.Here’s the uncomfortable truth: Agentic AI could widen, not close, healthcare disparities. I call it the agentic AI paradox. Many of these tools require expensive infrastructure, such as imaging equipment, stable networks and specialized software, that low-resource settings simply don’t have. Even if everyone benefits in absolute terms, the rate of improvement won’t be equal. Those hospitals and systems that already have advanced infrastructure will accelerate faster, leaving others behind. So, while the overall quality of care rises, the distance between populations grows wider. That’s the difference between equality and equity. Equality means everyone gets the same benefit. Equity means we tailor what’s given to what’s needed so that outcomes, not just inputs, become fairer. Without proactive governance, this paradox will only deepen.That's where we need AI governance councils, multidisciplinary groups that bring together clinicians, data scientists, ethicists and, more importantly, patients themselves. Fewer than 1% of healthcare AI studies right now include any kind of community engagement, so we’re designing systems based on what we think patients want, not what they truly need. I’ve been advocating for something I call the Global AI Equity Index, a framework that measures how fair, explainable and reliable these systems are across demographics and geographies. Every AI tool deployed in healthcare should perform equally well for every group of people it serves. We also need continuous validation across diverse populations. Models trained in urban hospitals shouldn’t be blindly applied in rural clinics. Without that layer of governance, agentic systems will simply inherit the biases of the environments in which they were born.So, where does that leave those of us on the front lines of care? Our role is evolving from information processors to strategic decision-makers and ethical stewards. Someday soon, an agent will round up, organize and summarize patient data far faster than any human. There are things it will never see, though. It won’t smell the alcohol on a patient’s breath or notice the fast-food bag they are holding. It won’t stop to ask a grieving patient how they’re really doing. Those are the moments that require judgment, empathy and context—humanistic aspects that no algorithm can replicate. Physicians of the future will supervise not just a team of humans but a mix of humans and agents, all working together toward better outcomes.Healthcare leaders can’t afford to wait. Agentic readiness begins with understanding. We need to invest in technology but also in literacy, governance and ethics. We need rigorous testing environments, safe sandboxes where agents can fail without causing harm, and we must build explicit guardrails so these systems never make unsafe recommendations, particularly in behavioral health or crisis care. First and foremost, we need community engagement. Patients, families and local networks must have a voice in the design, deployment and evaluation of these systems. Without that, the promise of agentic AI will never be equitably realized.Agentic AI is a profound advancement from AI that thinks to AI that acts. It will usher in a new frontier of medicine, wherein technology not only assists but participates. Yet, let us remember that progress without ethics can deepen the very inequities we hope to solve. But if we design with empathy, equity and oversight, agentic AI can become a catalyst for fairness and better health outcomes everywhere. The future of medicine will be co-created by humans and agents acting together, guided by judgment, compassion and care.
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