This year’s emerging trends in healthcare AI – so far

Healthcare technology continues to be a rapidly evolving space, driven by advancements in AI and other digital solutions. Health systems are keeping a close eye on the transformative potential of these innovations and how they can help support clinicians and improve patient outcomes. Here are three health tech trends that we’re watching right now.

The next phase of AI in healthcare: agentic systems at work 

You’ve probably heard conversations about generative AI in the news, on social media, in meetings and chats around the watercooler. But what about agentic AI? In the coming year, agentic AI will be moving into the spotlight, especially in healthcare. This type of AI – often referred to as AI agents – can provide clinicians with proactive support by operating with clinical context and intent to deliver adaptive, goal-directed support across clinical workflows [1]. Unlike traditional AI applications, agentic AI can operate within existing clinical systems, coordinating work across applications and teams while keeping healthcare professionals firmly in control of clinical decisions. These tools can help with tasks that often drain time and attention, such as preparing patient summaries, coordinating care across teams and surfacing missing or important patient information to ensure better, more effective treatment.

So what does this look like in practice? Take radiology, for example. Radiology is one of the clinical domains where digital innovation has had an early and visible impact, and the integration of agentic AI is no exception. Today, conversations about AI no longer focus on whether radiologists will be replaced; instead, they’re shifting to discussions about how these technologies can help augment human expertise as demand for imaging services increases. AI agents can help support imaging specialists by assisting with pre- and post-interpretive work, giving them back time to focus more on clinical judgment and patient interaction. In this way, agentic AI can support coordination and preparation, while clinicians remain responsible for diagnosis and care decisions. And interest in these tools is already high, with many clinicians reporting optimism about incorporating AI into their work – in fact, according to the 2025 Future Health Index, 85% of radiologists think that AI technologies could improve patient outcomes.

Healthcare systems across the globe are starting to invest in agentic AI, with several notable organizations taking the lead in implementing these AI agents. In the US, Mount Sinai Health System in New York and Minnesota-based Mayo Clinic are using these technologies to streamline workflows, automate repetitive tasks and enable more meaningful, personalized care [2]. The UK’s NHS recently launched a project focused on responsible, collaborative and sustainable deployment of agentic AI across the system [3]. Interest in AI agents is predicted to increase rapidly in 2026, as more healthcare systems – particularly in Asia, Australia and Europe – look to harness the power of these proactive, goal-driven AI collaborators [4].

Looking ahead, agentic AI is expected to become part of a hybrid healthcare workforce that works alongside clinicians to help health systems manage growing complexity, demand and workforce pressures.

Longitudinal data powers the intelligence layer of patient care

For years, the focus in patient monitoring has been how to connect data from one system to another. Now the emphasis is shifting to what hospitals do with that information once it’s flowing – moving beyond isolated check-ins toward continuous health tracking across care settings.

Longitudinal data creates a continuous patient timeline that follows individuals from the ICU to the general ward, through virtual care, and into the home. No longer limited to isolated snapshots of vital signs, clinicians gain visibility into trends, baselines, and prior interventions. This more complete, dynamic picture helps answer critical questions at the point of care, such as: “Is this normal for this patient?” or “Have we tried this therapy before?”

Making this possible requires open systems that aren’t locked into one vendor’s technology. Without common standards that allow medical devices to communicate, patient data remains scattered in silos. But when information from bedside monitors, transport equipment, portable diagnostics, and home sensors feeds into a single record, monitoring shifts from reaction to prevention.

AI and advanced analytics help turn that foundation of longitudinal data into clinical intelligence. By revealing patterns over time, predictive tools and smart alarms can enable earlier detection of deterioration while reducing unnecessary alerts – freeing clinicians to focus on delivering timely patient care.

Bringing AI into the heart of the procedure room

AI is no longer limited to analyzing medical information or supporting diagnostic decisions. It is being integrated directly into the procedure room, reshaping how complex interventions are performed in real time. This innovation brings opportunities for transforming minimally invasive, image-guided therapies, where clinicians must navigate tiny devices through moving anatomy. 

Traditionally, these procedures rely on clinicians mentally merging multiple imaging views from different modalities while coordinating across teams under intense time pressure. Advances in image integration, some of which are AI-enabled, are beginning to support clinicians during such complex procedures by enhancing real-time visualization of device position, trajectory and orientation.

For example, repairing a leaking heart valve via catheter is a technically demanding procedure, performed inside a beating heart. A new AI-enabled device tracking technology can now continuously visualize where a device is, how it is oriented, and where it needs to go, giving the entire team a shared, dynamic understanding of the procedure [5,6]. This added clarity becomes particularly valuable as advanced therapies expand beyond highly specialized centers, helping to make complex interventions accessible for more patients. 

Healthcare AI is rapidly evolving from standalone tools into intelligent systems that actively support clinicians across the care continuum, helping them reclaim time to focus on their patients. These innovations are helping to improve workflows, strengthen clinical decision-making and deliver better care for more people. As these technologies continue to develop, the question remains: what other AI trends will emerge in 2026? For now, it’s one to watch as the next wave of healthcare innovation unfolds.