The Watch That Cried Maybe: How PulseAI Is Ending the Era of Uncertainty

We used to think that only trained eyes in hospital rooms could detect heart rhythm problems. Then came the smartwatch.

In 2020, a man in his sixties walked into a cardiology clinic in southern Germany. He was feeling fine. No chest pain, no palpitations, no shortness of breath. But his smartwatch had been buzzing. Repeatedly. It kept warning him: possible AF detected. The clinicians ran a standard 12-lead ECG. He was, in fact, in persistent atrial fibrillation—and had been for weeks. He had no idea.

This isn’t an isolated story. It’s part of a growing body of evidence that pushed the European Society of Cardiology (ESC) to include something remarkable in their most recent guidelines: wearable devices—yes, your Apple Watch, your Fitbit, your Galaxy Watch—can be used to diagnose atrial fibrillation [1].

It’s not a passing mention. It’s a tipping point.

The Paradox of Precision

Now, let’s pause for a second.

When the ESC says these devices can be used to diagnose atrial fibrillation, they’re not saying this lightly. They’re not just tipping their hat to the tech world. They’re acknowledging something specific, something subtle—but important. The quality of the ECG tracings from smartwatches is, in many cases, clinical-grade. You can take a single-lead ECG using your Apple Watch and, when you compare it to a standard tracing from a hospital-grade machine, the waveform is often astonishingly similar. It’s not a toy. It’s not approximate. It’s real.

But here’s the catch, and this is where it gets interesting.

While the tracing might be good enough for a cardiologist to read, the algorithm interpreting that data? That’s a different story. Automated AF detection algorithms in wearables, even the best ones, tend to play it safe. They’re like overly cautious librarians, if they’re not absolutely sure about what they’re seeing, they won’t make a call. Instead, you get a vague message: Inconclusive. Try again.

Sometimes you try again. Sometimes you don’t. And AF keeps fluttering quietly in the background.

Studies have shown these inconclusive rates can be surprisingly high—anywhere up to 17% depending on the device and context. For every person who catches a potentially life-threatening arrhythmia early, there are many more left in limbo, unsure whether they should worry or just ignore the signal.

This, paradoxically, is both the promise and the problem of smartwatch diagnostics.

The PulseAI Breakthrough: From “Inconclusive” to Confidence

Here’s where PulseAI changes the story.

PulseAI is a deep-learning algorithm trained on over 15 million ECG tracings from real patients worldwide. Unlike device-native algorithms trained only on smartwatch data, PulseAI understands subtle rhythm patterns across diverse populations and devices.

When tested against leading wearable ECG algorithms—Apple, Fitbit, Samsung, and AliveCor—PulseAI reduced inconclusive readings from the typical 15-17% down to just 1-2%. That’s not a small improvement. It’s a leap.

By sharply reducing these ambiguous “I don’t know” responses, PulseAI transforms uncertainty into clear, confident answers.

Anxiety in the Absence of Answers

But here’s something we don’t talk about enough.

A lot of those inconclusive readings? They come from healthy people. People with no history of heart problems. People who were just curious—who tapped the ECG app because their watch said it could, or because they felt a momentary flutter after coffee. And then the screen goes blank. Inconclusive. Try again.

This ECG from an Apple Watch is an example of an inconclusive test. Distinct P-waves are visible, effectively ruling out atrial fibrillation. The physician's final interpretation was normal sinus rhythm, with artifacts attributed to unstable electrode contact.

Now imagine what that does to someone who wasn’t worried five minutes ago.

Suddenly, the watch that once reminded them to breathe is suggesting something might be wrong with their heart—but won’t say what. No diagnosis. No reassurance. Just ambiguity.

And ambiguity, for many people, is worse than a diagnosis. It breeds a kind of health anxiety that modern medicine is only beginning to understand: the emotional toll of digital uncertainty.

PulseAI doesn’t just help detect atrial fibrillation. It helps healthy people stay healthy, mentally and physically, by not scaring them needlessly. It knows when to speak, and when to stay silent.

In a world where devices constantly feed us data—steps, calories, heartbeats—clarity isn’t a luxury. It’s a necessity. And PulseAI delivers that clarity at the exact moment it’s most needed.

Because sometimes, knowing you’re okay is just as important as knowing when you’re not.

Results from the Basel Wearables study comparing PulseAI to manufacturer algorithms [2].

The Quiet Breakthrough

We tend to think of innovation as invention. The new device. The sleek screen. The extra sensor. But more often, the real breakthroughs happen invisibly—under the hood. In the algorithms. In the pattern recognition. In the quiet moments when a device decides what not to say.

PulseAI is one of those invisible breakthroughs.

It doesn’t need to be redesigned. It doesn’t need to add more electrodes or reinvent the user interface. Instead, it offers something much harder to build: trust.

Trust that the signal you’re seeing is meaningful. Trust that a healthy person won’t be misled into worry. Trust that an irregular rhythm won’t be ignored.

And it’s doing this not just for one device, but for many. PulseAI is being built for the next wave of smartwatch developers—for the companies still in stealth mode, the startups prototyping their first ECG sensor, the OEMs entering healthcare for the first time. These new players don’t have to start from scratch. They can build on a foundation of clinical intelligence. They can offer something better from the beginning.

Because in the end, it’s not just about detecting AF. It’s about delivering certainty, where uncertainty used to live.

And maybe, just maybe, that’s how the future of heart health begins. Not with a bigger screen, or a faster chip. But with an algorithm that finally knows what it’s looking at.

References

[1] G. Hindricks, T. Potpara, N. Dagres, et al., ESC Scientific Document Group 2020 ESC guidelines for the diagnosis and management of atrial fibrillation developed in collaboration with the European Association for Cardio-Thoracic Surgery (EACTS): the task force for the diagnosis and management of atrial fibrillation of the European Society of Cardiology (ESC) developed with the special contribution of the European Heart Rhythm Association (EHRA) of the ESC

[2] Weidlich S, Mannhart D, Kennedy A, Doggart P, Serban T, Knecht S, Du Fay de Lavallaz J, Kühne M, Sticherling C, Badertscher P. Reducing the burden of inconclusive smart device single-lead ECG tracings via a novel artificial intelligence algorithm. Cardiovasc Digit Health J. 2023 Dec 27;5(1):29-35. doi: 10.1016/j.cvdhj.2023.12.003. PMID: 38390580; PMCID: PMC10879015.