AI could bring earlier intervention to patients between doctor visits

Narrowly designed AI systems could help clinicians detect deterioration, monitor patients at home and catch post-surgical complications sooner, but regulation and trust remain significant hurdles.

Artificial intelligence is increasingly being treated as a source of danger, but in health care it may also offer one of the clearest routes to better outcomes. In commentary for The Fulcrum, Robert Pearl argues that the public debate has become so focused on the risks of advanced AI that it risks obscuring a more immediate and practical possibility: tightly designed AI systems that help clinicians spot trouble earlier, follow patients more closely and intervene before conditions worsen. That argument fits with broader industry thinking. IBM’s healthcare analysis says AI agents can plan, coordinate and act across clinical and administrative workflows, while other sector guides describe their promise in reducing repetitive work and improving patient engagement.

Pearl’s case rests on a simple clinical point: when doctors have more frequent and better-quality information, they can act sooner. He points to postoperative patients, people living with heart failure and those managing long-term conditions such as diabetes and hypertension. The potential advantage is not that machines replace doctors, but that they extend the reach of medical teams between visits. Industry write-ups from companies including Uptech, Prioxis and ReveChat make the same general point, saying AI agents can help with monitoring, follow-up, documentation and other routine tasks that often fall through the cracks in busy health systems.

That logic matters because chronic disease remains a huge burden. Pearl cites U.S. figures showing millions of Americans live with heart failure, high blood pressure or diabetes, and argues that many of these patients are still assessed only intermittently in the doctor’s office. He says that is a poor fit for conditions where small changes in weight, swelling, glucose readings or blood pressure can signal a larger problem. BiomedAtlas’s 2026 guide on agentic AI in healthcare describes the same model of care, in which systems retrieve data, assess it, recommend action and hand over to humans when intervention is needed.

The same approach could also improve care after surgery. Pearl says patients at home often struggle to tell whether redness, drainage or pain around an incision is part of normal healing or the beginning of an infection. AI tools that analyse photos, video and symptom reports could flag likely complications more quickly and send them to clinicians for review. That matters because, as he notes, surgical-site infections add billions of dollars a year to US health care spending. Health technology guides from several firms similarly argue that agentic systems could help with post-operative follow-up, cross-check symptoms and reduce the delays that come with relying only on patient self-reporting.

Pearl also argues that the main barrier is not technical feasibility but regulation, payment and trust. He says the United States needs a structured environment in which these tools can be tested, monitored and approved without exposing patients to unnecessary risk. That is consistent with other healthcare AI analyses, which stress the need for robust cybersecurity, secure data handling, system integration and human oversight. In Pearl’s view, the goal should be neither blind enthusiasm nor blanket fear, but a careful effort to use narrow AI applications where they can save time, reduce avoidable harm and bring care closer to the patient.