What’s Next for AI in Digital Health in 2027?

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People relate to their health differently than they did even two years ago. They don’t wait for an appointment to ask a basic question. They ask an AI, get an answer in seconds, and arrive at their visit already informed.

Having built consumer technology serving billions of users at Google and Warner Bros. Discovery, I now lead product and technology at OpenLoop. Working closely with our clients, clinicians, pharmacies, and operations teams gives me a firsthand view of where AI can improve care—and what it takes to deliver those improvements at scale. 

That opportunity is what excites me about OpenLoop’s mission: personalized, adaptive, and holistic care that is easy to access, delightful to experience, and fits into people’s lives. We build the technology and operations that help our clients deliver that care under their own brands, giving clinicians more time for patients and reducing friction across the care journey.

Heading into 2027, the most important AI trends in healthcare share one theme: AI is moving into the core of how care gets delivered, making this vision possible at scale.

The four AI trends in healthcare that will define 2027

Each of these is already underway. What changes in 2027 is scale.

1. Personalized, low-friction access becomes the expectation

Patients expect care to be as responsive and easy to navigate as the rest of their digital lives.

The consumer relationship with health is changing quickly. With tools like ChatGPT and Gemini, people can explore health questions, understand unfamiliar terms, and prepare for conversations with their clinicians in seconds. That raises expectations for what comes next: care that understands their context, adapts to their needs, and makes the next step clear.

• Nearly one in three U.S. adults use AI chatbots for health information, a share that has almost doubled in two years.
• More than 80% of physicians use a wearable themselves, and almost all review patient wearable data. Still, fewer than 6% have integrated that data into their clinical workflows.

Patients have more information and health data than ever, but turning it into coordinated care still takes too much work. This is where OpenLoop is investing: connecting biomarkers, sleep, diet, and fitness data to help inform personalized next steps, with clinical guidance.

I have seen the opportunity firsthand while testing an OpenLoop-enabled care pathway: a Saturday lab draw, a virtual visit on my schedule, and AI helping me understand unexpected results and develop a plan with my provider. I loved how the care journey fit into my life rather than making me rearrange my life around healthcare.

That is the experience we want every patient to have: personalized, adaptive, and holistic care, with clear guidance and less work to manage it.  Combined with AI’s role in at-home diagnostics, this is how AI helps virtual care shift from reactive to proactive.

2. Clinician AI assistants, with a human always in the loop

Clinician-facing AI should take work off doctors’ plates, not replace them.

Two developments make this practical now. Models are far better at grounding their answers in trusted information, and agentic AI, systems that can take action rather than only answer, is becoming common. Together they let us build assistants that genuinely help a clinician instead of adding noise.

Here is what that looks like in OpenLoop’s work:

• Grounded in real protocols. OpenLoop’s clinician network is trained on 100+ clinically-vetted protocols, which gives these assistants a reliable foundation to reason from, with safety limits and oversight built in.
• Focused on the decision. The assistant helps a clinician work through a patient’s intake and the signals in front of them, surface the actions available, and identify the right personalized treatment options for that individual.
• The clinician stays in control. Their time goes to confirming the recommendation is accurate, adjusting it, and the direct patient relationship, the part only a clinician can do.

Hallucination used to make this impractical, and it is still a real risk. But we now have better ways to manage it. Post-training, careful prompt design, evals, grounding techniques, and the right guardrails help reduce how often errors happen and limit their impact, even when we build on off-the-shelf models.

That is what moves clinician AI from interesting to usable at scale. It is also what actionable AI means in practice: a grounded assistant that makes a skilled clinician measurably faster, rather than another chatbot added on top of the workflow.

3. AI moves into the pharmacy and supply chain

Some of the biggest operational gains sit downstream of the clinical visit.

Most of the AI conversation stops at the clinical encounter. At OpenLoop, our integration into pharmacy and fulfillment gives us a direct view of the work that follows: anticipating demand, preparing and dispensing medication, and getting it to the patient.

Think of it as cloud-like elasticity for care delivery. Intelligent routing connects patients to appropriately licensed clinicians and approved prescriptions to pharmacies with available capacity. As demand shifts, we can draw on capacity across the network, helping reduce bottlenecks and keep care moving.

Applying AI across that chain pays off in three ways at once:

• It shortens the time from a clinical decision to medication in the patient’s hands
• It lowers cost for the brands building on our infrastructure
• It reduces friction in the patient experience

The value compounds. The patient gets faster fulfillment and a better experience, and the brands building on our infrastructure get real cost efficiency at the same time. This is one of the clearest places AI can cut operational costs while improving care, not trading one against the other. The brands that lead in 2027 will treat the supply chain as part of the product, because delays and errors there directly shape how patients judge the service.

4. Support that resolves faster, with humans where they matter

Automate the routine so people are free for what actually matters.

Healthcare support is still overwhelmingly human and call-center-based, often run through outside BPOs. Ultimately, there is a sound reason for that: when something feels wrong with your health, you want to talk to a person. The friction lies in the wait and the repeated back-and-forth.

That is what is changing:

• Advances in voice AI and support automation now resolve routine questions about billing or scheduling quickly and accurately, freeing support teams to handle more complex needs.
• When a patient does need a person, they reach one quickly, without long queues or having to explain themselves again.

OpenLoop is investing here so patients get the information they need in a timely, responsive way, and human attention goes to the moments that change how a patient feels and what happens next. The same approach is already transforming healthcare administration more broadly.

2027 goes beyond point solutions: AI at scale

These four trends point to the same shift. We are moving past siloed, tool-by-tool AI toward AI that runs across the whole system of care and is largely invisible to the user. Two ideas sit behind how we get there.

• Redesign from first principles. You get limited value from bolting AI onto steps you already run and asking it to go faster. The larger gains come from rebuilding the workflow to assume AI from the start. That matches how we’re building at OpenLoop: the technology is part of the operation, not an addition at the end. It also fits what clients actually want. They care that their patients get the right services and outcomes, delivered efficiently and with consistently high quality. The steps behind that can be rewritten entirely, as long as those results improve.
• Protect data quality. Garbage in, garbage out. No amount of AI spending makes up for weak inputs. The right medical protocols and experienced clinical advisors are what keep a system accurate and safe, no matter how capable the model is.

For a broader view of how these pieces connect, this guide to AI’s impact on cost, access, and outcomes is a useful starting point.

Responsible AI has to come first

If 2027 is the year AI becomes common in healthcare, it also has to be the year responsible AI becomes the default. The encouraging part is that the tools are improving quickly. Systems are much better at grounding their answers, retrieving accurate information, and enforcing the guardrails you set.

The risk has not gone away. Small, deliberate safeguards make a measurable difference. The same techniques we rely on internally, including post-training, prompt design, and guardrails, are what move hallucination from a dealbreaker to a managed risk.

Governance is becoming a priority heading into 2027, and that is the right instinct. Responsible AI works best as a decision you make early in the design:

• Flag questionable input
• Respond with caution
• Keep a human in the loop

Teams that build this way earn trust over time, and that trust is hard to recover if they skip it. 

What I’m most excited about for AI in 2027

A few things have me genuinely optimistic about the year ahead, beyond the trends above.

1. The first is AI becoming a seamless, trusted part of the health experience. At its best, this technology makes care faster and easier without getting in the way, and with people always in control of their own information. We aren’t fully there yet, but we’re close, and the move from “using an AI tool” to “the experience just works” is the shift I’m watching most.
2. The second is personal. I bike, I consider myself reasonably healthy, and recently I ran some lab work while testing one of our own flows. A few results came back flagged, and they weren’t ones I expected. Rather than leaving me to sit with the worry, AI helped me turn those numbers into a concrete, sustainable plan. Managing my own health felt lower-lift and, honestly, more motivating. That is the experience I want every patient to have.
3. The third is partly selfish: physical AI. In pharmacies and fulfillment, physical automation is going to be enormous for how quickly and reliably care reaches patients. And at home, I’m still waiting for the robot that handles the dishes and the laundry. We already have AI writing poetry. I’d gladly trade some of that for a clean kitchen!

That combination, serious impact on infrastructure and real improvement to everyday life, is what makes this moment so energizing.

What this means for digital health leaders

Put it together and 2027 comes into focus:

• Patients arrive informed and expect proactive, personalized care
• Clinicians work alongside grounded AI assistants that take work off their plates
• Pharmacy and support operations run faster and at lower cost
• All of it depends on designing for AI from the start, maintaining clean data, and building in governance

None of this works as a set of separate tools. It works only when the underlying infrastructure is built for it.

That is what we focus on at OpenLoop. AI is built into the infrastructure itself, powering a nationwide clinician network, pharmacy fulfillment, and 24/7/365 patient support rather than layered on top. That is how to scale AI, not as a separate tool, but as an integrated system.

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However you’re approaching virtual care, our team can help you launch or scale it under your own brand. Book a call to talk it through.