AI in Healthcare Is Growing Up: 5 Trends That Will Matter in 2026

Healthcare has spent the past two years talking about artificial intelligence. 2026 will be the year it starts proving it.  

The industry is moving far beyond pilots and prototypes and into a phase where AI is expected to deliver measurable results: improved outcomes, reduced costs, and tangible operational relief. A new report from Silicon Valley Bank finds that AI represented 46% of healthcare technology investments in 2025, even as total sector investment fell 12% to $46.8B. 

As organizations push past early experimentation, here are five trends that will shape how AI actually takes hold in healthcare in 2026. 

1. AI Will Shift from Model-Centric to Infrastructure-Centric 

The last wave of AI innovation focused heavily on models. The next wave will focus on everything underneath them. 

Health systems are realizing that even the most sophisticated model is only as effective as the data, identity, and workflow infrastructure supporting it. In 2026, differentiation won’t come from who has the “best” algorithm, it will come from who can operationalize AI consistently across environments, sites, and use cases.  

This shift is already changing investment priorities. Tech leaders are moving away from standalone model evaluation and toward foundational capabilities such as interoperability, identity resolution, governed APIs, and secure data access. AI success will increasingly be determined by architectural readiness, not experimentation alone. 

2. Multimodal AI Will Become Healthcare’s Inflection Point 

Text-based or claims-based AI can only go so far. The most meaningful breakthroughs in 2026 will come from multimodal AI; systems that combine clinical notes, labs, vitals, scheduling data, device signals, and behavioral inputs, along with large imaging, genomic and streaming device data sets will have a bigger impact. 

This is where AI begins to surface risk earlier, coordinate care more precisely, and automate workflows end to end. But multimodal intelligence introduces a long-standing healthcare challenge: connecting siloed systems in real time while ensuring data accuracy and patient identity integrity. 

Organizations that can unify data across modalities, accurately, securely, and with consistent semantics, will be the ones that unlock AI’s full potential. Without that foundation, multimodal AI remains aspirational rather than operational. 

3. Agentic AI Will Quietly Automate Healthcare’s Micro-Workflows 

Large language models opened the door. Agentic AI will begin doing the work. 

In 2026, AI agents will increasingly handle the small but critical tasks that slow clinical and operational workflows: gathering prior records, checking eligibility, easing integration, routing referrals, requesting missing documentation, or updating patient summaries. 

This isn’t AI making diagnoses or replacing clinicians or developers. It’s AI removing friction, reducing clicks, shortening cycles, and returning time to healthcare teams.  

The real challenge will be governance. AI agents must be able to access the right systems, understand appropriate context, and act within clearly defined boundaries. Organizations that treat agents as extensions of existing workflows (rather than standalone tools) will be far more successful in deploying them safely and sustainably. 

4. AI-Ready Data and Secure Data Will Converge 

As experimentation accelerates, particularly with AI-native interaction patterns, healthcare leaders are confronting a critical realization: the standards for AI-ready data and secure data must be the same. 

That gap is already showing up in governance practices. A 2025 HIMSS report, found that 47% of organizations have approval processes in place for AI technologies, while 42% do not (and 11% were unsure). As AI use expands, organizations will need clearer policies, monitoring, and accountability, not just more tools.  

In 2026, AI governance and cybersecurity will converge. Leaders will demand clear visibility into how data moves, who or what is requesting it, and whether that access meets regulatory, clinical, and security expectations. Zero-trust principles, granular audit trails, and strong API governance will become table stakes, not optional enhancements. 

AI cannot be layered on top of opaque or loosely governed data environments. Trust, both clinical and organizational, will depend on transparency and control. 

5. AI Will Force Healthcare to Future-Proof Its Core Infrastructure 

After years of what many call “pilot purgatory,” 2026 will mark a turning point.  

Health systems and digital health companies will begin modernizing the foundational infrastructure that has historically slowed transformation: identity data management, integration patterns, network connectivity, and API maturity. 

This work isn’t glamorous, but it’s essential. AI cannot scale across hundreds of sites if identity is unreliable, if integrations are brittle, or if access breaks under load. The industry is coming to terms with a simple truth: scalable AI depends as much on stability at the center as innovation at the edges. 

A More Mature AI Era 

2026 will not be the year AI replaces clinicians. It will be the year AI begins supporting clinicians and operations in ways that are measurable, safe, and durable. 

The organizations that succeed won’t be the ones chasing the flashiest tools. They’ll be the ones building the foundation AI needs to work, like clean identity, connected data, secure access, and workflows designed for intelligent automation. 

AI’s future in healthcare isn’t magic. It’s maturity.