Step on a scale at a doctor’s office and your health usually gets summed up in one number. Weight divided by height squared. That’s your BMI, and it’s been the default health metric for almost 200 years.

It was never built for that job. BMI was invented in the 1830s as a statistical shortcut for studying populations, not for telling an individual person anything meaningful about their body. It can’t tell the difference between a muscular athlete and someone who’s out of shape but happens to weigh the same. It ignores bone density, body water, and where fat actually sits in the body, which matters more than most people realize.

The metrics that actually matter are the ones BMI was never designed to catch: how much of your body is muscle, how much is fat, and where that fat is sitting.

The muscle question BMI can’t answer

Skeletal muscle is the body’s metabolic engine. It’s what keeps your resting metabolic rate up, what protects your joints, and what determines how well you move as you get older. Losing it quietly, without knowing, is one of the bigger blind spots in how people track their own health.

Fat isn’t one thing either. Subcutaneous fat, the kind just under the skin, carries far less risk than visceral fat, which wraps around internal organs and is directly linked to cardiovascular problems. A person can lose weight and still be carrying dangerous levels of visceral fat. BMI has no way of showing that.

This is also why short-term weight drops can be misleading. A lot of what shows up on the scale in the first week or two of any diet is water loss, not real change in body composition. The number moves, but it doesn’t mean much yet.

Why this is showing up in headlines right now?

Part of why this conversation is happening now, rather than five years ago, comes down to the rise of GLP-1 weight-loss drugs like Ozempic, Wegovy, and Zepbound.

GLP-1, or glucagon-like peptide-1, is a natural gut hormone that signals fullness to the brain. These medications mimic it, slowing digestion and dulling appetite, which lets people eat significantly less and lose weight fast.

That speed is exactly what exposed the problem. Rapid weight loss burns fat and muscle together, not just fat. Lose 20 pounds on a GLP-1 drug, and both the scale and a BMI calculator will call it a clean win.

If a meaningful chunk of that was skeletal muscle, metabolic rate drops, joint support weakens, and the risk of physical frailty goes up, none of which shows up in that one number.

Dr. Hon Pak, Head of Digital Health Globally at Samsung Electronics, has pointed out that with GLP-1 therapies in the picture, raw weight lost can’t be the metric that defines success anymore. The focus has to shift to what kind of weight is being lost.

Kevin Duffy, CEO of iFit, has made a similar point: BMI stuck around because it’s easy to calculate from just height and weight, not because it’s the right thing to track. Muscle mass and body fat percentage matter far more, they’ve just historically been hard to measure outside a lab.

What your watch can measure now

Getting real numbers on muscle mass and visceral fat used to mean an expensive clinical scan. That’s the part that’s changed.

Samsung has built Bioelectrical Impedance Analysis sensors into the Galaxy Watch, sending a low-level microcurrent through the body to estimate skeletal muscle mass, body fat percentage, body water, and basal metabolic rate, all from your wrist.

For anyone losing weight, whether through a GLP-1 medication or a more traditional route, that gives real visibility into whether the weight coming off is fat or muscle.

It’s the kind of feedback that lets someone actually adjust, adding resistance training or upping protein intake, instead of finding out the hard way months later.

Samsung pairs this with Samsung Food, which uses AI to look at what’s actually in someone’s kitchen and suggest protein-forward meals that support muscle retention.

Karthik Poriya, Head of Product at Samsung Food, has noted that people tend to fixate on small day-to-day food decisions when what actually moves the needle is the trend over weeks and months. AI is what makes it possible to zoom out and see that trend instead of getting lost in it.

Combine body composition data with sleep tracking, activity monitoring, and a broader Energy Score, and the whole framework shifts. It’s no longer about getting lighter.

It’s about getting stronger, protecting muscle, and extending healthspan rather than chasing a number that was never built to measure any of that in the first place.

Most of the conversation around artificial intelligence right now revolves around chatbots. Tools that write your emails, summarize long PDFs, or spit out blocks of code in seconds. That is the AI most people interact with daily.

Behind the scenes in health research labs, however, a profound shift is taking place that has nothing to do with words.

Scientists are training AI models to read raw human biology instead: optical heart rate signals, ECG readings, movement patterns, and sleep data. The goal is teaching AI to understand what is happening inside your body long before you ever feel sick.

During Samsung’s panel discussion on the future of Connected Health at the Pan Pacific London, Otavio Penatti, Samsung’s Head of Health AI R&D, captured this vision simply: “Your body speaks, Samsung Health translates.”

So what makes this different from your average fitness app?

Most health apps you already use run on basic rules. Your heart rate crosses a certain number while you’re resting, so the app sends an alert. That’s about as sophisticated as it gets.

These new models work on a different level entirely. Researchers feed them massive amounts of raw biometric data without labeling any of it first.

No doctor has to sit there and tag every second of heart rate or movement data by hand. The AI figures out the patterns on its own, essentially learning the basic language of how a human body functions.

Once a model has been trained on millions of hours of this kind of data, it can then be fine-tuned for a long list of specific medical uses.

And it’s not just watching for one odd spike in your heart rate. It’s cross-referencing your heart rate variability, skin temperature, movement, and sleep patterns all at once to build a detailed picture of what’s normal for you specifically.

One night of sleep data, over 130 diseases

Here’s where it gets interesting. Researchers at Stanford built a foundation model trained specifically on sleep data, using both raw polysomnography (the kind of detailed sleep study done in a lab) and data from wearables.

When they compared just 24 hours of sleep data against years of longitudinal health records, the model was able to flag onset risk for more than 130 different diseases. From one night of sleep.

Dr. Hon Pak, who leads Digital Health globally at Samsung, described where this is headed: “We are collecting data right now in which we have clear signals of things to come. We just don’t know what it is right now. But through the work that we’re doing, we’ll be able to share these insights early and in the right, meaningful way.”

The missing link: your wearable talking to your medical chart

Right now, your Apple Watch or Galaxy Watch has no idea what your last blood test looked like. Your wearable data and your actual medical records live in two completely separate worlds.

That’s the real opportunity here. If continuous biosignal data from your wearable gets linked with your actual health records, prescription history, and lab results, AI could start catching subtle shifts in your body that even your doctor might miss during a routine checkup.

Not because your doctor isn’t paying attention, but because a once-a-year appointment simply can’t compete with data collected every single day.

Instead of catching a heart attack or a metabolic crisis after it’s already happened, the idea is to catch the early warning signs years before that. It’s a shift from reacting to illness to catching it early enough that it might never become a crisis at all.

It’s worth noting that most of this is still in research stages. How it eventually shows up in the health apps on your phone will depend a lot on how these companies handle the data, and how accurate these predictions actually turn out to be in practice.

But the direction is clear enough: the next major leap in AI might not be about how well it writes. It might be about how well it listens to you.

Here’s a paradox worth sitting with: Wearable adoption keeps climbing. A lot of people now walk around with a sensor on their wrist, tracking heart rate, sleep, steps, sometimes ECG. And yet chronic disease rates haven’t moved.

That’s the problem Dr. Hon Pak, Samsung’s Head of Digital Health globally, opened with at a recent panel on the future of connected care. More data hasn’t meant better healthcare. His diagnosis: a first-mile, last-mile issue.

The first mile is that all this wearable data rarely makes it to a doctor in any usable form. The last mile is that even when a doctor says “lose weight, eat better, move more,” that advice tends to dissolve the moment real life takes over.

Picking up kids, caring for parents, getting through a Tuesday, it’s not that people don’t know what to do. It’s that knowing and doing are two different problems.

Samsung’s answer involves three things it says connected care needs to mature: meeting people where they actually are, building enough trust that people rely on the data, and using AI to turn that data into something a person can act on rather than just look at.

The Tuesday evening problem

Karthik Poriya, Head of Product at Samsung Food, framed the everyday failure point clearly. Picture it: long day, you’re tired, staring into the fridge. You’re not reaching for the healthiest option in that moment, you’re reaching for whatever’s fastest and most satisfying.

For years, nutrition tracking has focused on logging what already happened. Poriya’s team wants to intervene right at that decision point instead, pairing Samsung Health’s underlying data (BMI, antioxidant index, glycation markers) with Samsung Food’s recipe index of roughly 40,000 dishes mapped across 34 nutrients, so a health number turns into an actual dinner suggestion rather than another stat to check later.

Dr. Pak backed this with a small, concrete example from his own family: moving leftover cheesecake out of eye-level in the fridge and putting washed, sliced carrots there instead.

Kevin Duffy, CEO of connected fitness company iFit, made a similar point about exercise. The most effective tool for sticking to a fitness plan is a personal trainer, he said, citing roughly 80% higher adherence compared to going it alone.

The problem is cost: US$100 an hour or more in a major city puts that out of reach for most people. iFit’s bet is that AI can deliver something closer to a personal trainer’s precision and motivation at a price that isn’t elitist.

Duffy pointed to three reasons his company partners with Samsung specifically: reach (Samsung hardware is already in people’s homes, on their wrists and TVs), access to the biomarker data needed to build an accurate plan, and the fact that fitness goals don’t exist in isolation from sleep and nutrition data.

Trust has to come before any of this works

None of the behavior-change ambitions matter if people don’t trust the data or the company holding it. Rohit R. L., who heads Samsung’s Technology Innovation Lab in the UK, laid out how his team approaches that.

Privacy comes first: Samsung, he said, treats itself as a custodian of user data rather than an owner of it. On top of that sits clinical validation, meaning published, peer-reviewed evidence that a feature actually works in the real world, not just in a lab.

He gave a specific example: Samsung’s ECG and irregular heart rhythm notifications have been clinically validated and cleared by regulators to detect signs of atrial fibrillation. He was careful with the wording there, detect signs of, not diagnose, since that distinction matters both medically and legally.

A separate study on fall detection for elderly users turned up a more human finding. The detection algorithm worked well in testing, but in real life people don’t wear their watches around the clock, and falls don’t happen on a schedule.

Dr. Pak drew a useful comparison: nobody tells their MRI technician they’re going to fidget through the scan, but with a wearable, you don’t get to control how or when someone wears it. Real-world validation has to account for that.

On the infrastructure side, Rohit described a three-layer privacy approach: on-device protection through Samsung’s Knox security framework, GDPR-compliant data handling, and alignment with the European Health Data Space, including a decentralized clinical trial system where patient identity stays with the hospital rather than moving to Samsung’s side.

Where AI actually comes in

The most technically dense part of the discussion came from Otavio Penatti, who leads Samsung’s Health AI R&D team in Brazil.

His team works on foundation models, the same category of large-scale AI models trained on unlabeled data that underpin most of today’s popular AI systems. Trained once on a huge amount of sensor data, these models can then be fine-tuned for many specific health applications, which tends to make them more accurate than models built for a single narrow task.

Penatti’s team is training these models on wearable sensor data (PPG, ECG, accelerometer) with the goal of getting the model to, as he put it, understand the body’s own signals well enough to translate them into something useful. Longer term, he sees potential in correlating that sensor data with clinical records to flag disease risk before symptoms show up.

Dr. Pak added a striking data point from Stanford research: a foundation model trained on sleep study data was able to look at just a 24-hour window of sleep and predict risk across more than 130 different diseases.

His takeaway was that the signals for a lot of future health problems are already sitting in the data being collected today, we just don’t yet know how to read all of them.

He closed this part with a number worth sitting with. A recent American Medical Association survey found that 97% of physicians have looked at wearable data at some point. Only 15 to 16% actually use it in day-to-day practice.

The top reason cited wasn’t distrust of the data, it was that the data doesn’t plug into existing clinical workflows. That’s part of why Samsung acquired health data platform Xealth, to build a pipeline that gets wellness data into the systems doctors already use.

Five years out

Asked to look five years ahead, each panelist’s answer tracked closely to their own corner of the problem. Poriya described a proactive dietitian in your pocket, one that doesn’t wait to be asked.

Rohit pointed to connected care that clinicians actually trust and patients fully control. Penatti envisioned AI that continuously links everyday behavior to clinical outcomes, catching problems before they start. Duffy’s version was a constant personal wellness coach, sifting through the data noise to tell you exactly what to do next.

The throughline across all four answers is the same: less raw data, more action. Samsung’s panel made the case that the wearable industry has spent the last decade solving for collection, more sensors, more metrics, more dashboards, while the harder problem, turning that information into something a tired person actually does on a Tuesday night, has barely been touched.

Whether foundation models and better clinical integration actually close that gap is still an open question. But it’s clearly the one Samsung is choosing to spend its next five years on.