AI Health Coaches: The Next Frontier in Wearables or Privacy Nightmare?

I’ve been tracking biometric data about my body since what feels like the dawn of time (or at least the dawn of wearables). I ran a half-marathon with the first Fitbit tracker, reviewed the very first Apple Watch and used the first smartphone-connected thermometer for ovulation tracking back when it was a pen-and-paper operation for most. 

Collecting data about my body isn’t just second nature; it’s practically part of my job description. And for years, it’s been entirely on me to overanalyze that mountain of metrics and figure out how to turn it into something useful.

So when AI health coaches started surfacing from Google, Samsung, Apple, Oura and others, promising to shoulder that mental load, I was all in. You mean to tell me I don’t have to decode every tiny fluctuation in my data on my own anymore? 

Most of us can’t afford a real-life wellness coach to meal-prep for us, hype us up midworkout or pry the dumbbells from our fever-wrought hands when we’re at the gym looking like a walking Flonase commercial. An AI coach felt like the next best thing: a nerdy, data-obsessed friend living in my phone, armed with years of my biometrics and the patience to explain them without judgment.

Over the last year, I tried them all, or at least the early versions of what they’ll eventually become. Personal trainers built into fitness apps. Chatbots tucked behind wearable dashboards. Coaches that whisper advice into your earbuds or nudge you from your smartwatch. Some free, some paid. 

Some of these features feel promising. Meta AI, for example, can read your Garmin heart-rate data into your ear through the glasses’ speakers so you don’t have to take your eyes off the trail. Or you might get training and rest-day recommendations based on how you slept and other physical data. 

Other features, however, still feel half-baked. Samsung’s running coach, for example, offered a one-size-fits-all training plan that didn’t match my goals or experience.  

In theory, these models should improve over time as they learn individual patterns and as people like me find better ways to leverage them. For now, though, most remain in their infancy, far from the full potential they’re meant to be: an always-available adviser, designed to make sense of the ever-growing pile of health data collected through wearables.

Best-case scenario: AI to the rescue

The current health care model in the US is overdue for a transformation. The system is overburdened, prohibitively expensive and facing demand that outpaces supply, especially in rural areas with limited access to doctors and medical equipment.

Dr. Jonathan Chen, professor of medicine and the director for medical education in artificial intelligence at Stanford, is optimistic that AI could play a constructive role in easing some of that pressure, especially when it comes to making sense of all the health information and clinical data in patient records. 

“We already have ways to collect data for people all the time, but even your doctor doesn’t know what to do with all that data in the ICU, let alone all the wearable data,” says Chen.

AI, he argues, can help bridge that gap by synthesizing information in ways that actually matter, such as flagging warning signs of potentially life-threatening conditions like hypertension before they become fatal. Having a personal health concierge at your fingertips could help you focus more intimately on wellness and encourage behavioral changes that reduce the risk of chronic illness over time.

“Even though the actionable insight might not be that different,” said Chen, “when it feels personalized, that might be a way some people will engage deeper.” Chen emphasizes that AI works best when it drives better conversations, not when it replaces them. He points to glucose monitoring as an example: Instead of walking into an appointment with a month of raw data, AI could review that information ahead of time and surface patterns and actionable insights to guide the discussion.

I’ve seen that best-case scenario play out firsthand. A close family member began receiving irregular heart rhythm notifications from an Apple Watch. The alerts had never appeared during a routine doctor visit, nor after wearing a clinical heart monitor at home for weeks. When the watch flagged an episode in real time, he got in front of a doctor, confirmed the diagnosis with an ECG and took action. A few months later, he underwent a heart procedure that significantly reduced his risk of a potentially life-threatening event. In that case, the wearable didn’t replace medical care, but did exactly what it was meant to do: surface a signal, start a conversation and help close a dangerous gap in care.

A recent privacy analysis by the Electronic Privacy Information Center found that the health-related data people assumed was private (including searches, browsing behavior and information entered into health platforms) is often collected and shared far beyond its original context. In one case, data entered on a state health insurance marketplace was tracked and sent to third parties, such as LinkedIn, for advertising purposes. Much of this information falls outside HIPAA protections, meaning it can be legally repurposed or sold in ways consumers never intended.

Even when anonymized, health data can often be traced back to a real person and even used by insurance agencies to raise premiums. 

“You can deidentify and can make it harder to tell, but if someone tried really hard, it’s actually not that hard to use statistical methods to reconstruct who’s actually who,” says Chen. 

Data breaches and hacks are just the tip of the iceberg. We often have little visibility into how long data will be stored, who it might be shared with or where it could end up years down the line. Chen points to 23andMe as a cautionary tale. The company had promised privacy and security, until financial trouble put massive amounts of genetic data in jeopardy.

“They’ll keep it secure and private, but then they go bankrupt. And so now they’re just going to sell all their assets to whoever wants to buy it.”

AI health coach: friend or foe?

The reality, at least in the short term, is likely less extreme than either of those scenarios. We’re probably not on the verge of AI saving health care, or of selling our most sensitive health data to the highest bidder. 

As Verspoor points out, the pay-to-play model isn’t exclusive to AI health coaches. Tech companies have been using personal data to power products long before generative AI entered the chat. Your search history may not look like an ECG, but it can be just as revealing about life stages, health anxieties or illness history. 

With AI health coaches having a direct line to real-time biometric data, it’s more important than ever for people to pay close attention to what data they’re signing off on and who they’re handing it to. Is that information staying on-device? Is it being shared with third parties? And what happens to it down the line? This requires people to be in the driver’s seat when signing up and to read the fine print, even if it means having to copy and paste it into yet another AI chatbot to translate the legal jargon. Then weigh whether the exchange is worth it to you. 

Chen believes the potential upside still outweighs the risks, especially if these tools succeed at getting people to care more about their health and engage with it more often. That engagement, he argues, is where the real value lies so long as AI remains a supplement to care, not a substitute for it. Both experts agree AI health coaches should function as ancillary tools to help you understand your data, ask better questions and jump-start conversations with your doctor. 

AI coaches may know your day-to-day vitals, but they still have blind spots when it comes to real-world context and medical-grade testing. Their advice, no matter how innocuous and obvious it may sound, like “hydrate after a bad night of sleep,” should be taken with a healthy dose of skepticism. Unlike tools such as ChatGPT or Google’s Gemini, some AI health coaches, including Google’s Fitbit Coach and Oura’s Advisor, don’t clearly cite sources or explain where their recommendations come from, at least not yet.

The tipping point 

The reality, at the moment, is less dramatic than either of these extremes. We’re probably not on the brink of AI saving health care, or of plummeting into a full-blown medical data dystopia. Instead, we’re in this awkward in-between phase. 

I was initially excited about the idea of an AI health coach taking some of the mental load off interpreting my health data. That quickly turned to skepticism as the privacy trade-offs became apparent. Now, after months of testing, I’ve landed somewhere else entirely: Most days, I forget the tool is there in the first place. 

That gap between insight and action is something human coaches have long understood. Jonathan Goodman, a fitness coach and author of Unhinged Habits, says AI excels at processing data, but behavior change rarely hinges on perfect metrics or the perfect training plan. 

“For a general-population human who just needs to move a little bit more, eat a little bit better, and play with their kids, it’s probably closer to 10% technical and 90% psychological,” he says. Metrics can surface patterns, but coaching is about asking the right questions, fitting movement into real life and recognizing those moments when someone is ready to push themselves into real transformation. 

To me, it’s that in-the-moment guidance, pushing me past my limit or telling me when to scale back, that’s missing from these AI coaches. The experience is largely passive, often requiring you to check the app to see that day’s training plan. Apple’s Workout Buddy might be the closest to that, with real-time motivation based on your stats, but even that stops short of actual coaching. And none has proven indispensable enough to make me seek it out consistently. 

To reach that tipping point, these companies will need to give us stronger reasons to engage and clearer safeguards to justify handing over our deeply personal health data.