I let ChatGPT analyze a decade of my Apple Watch data, then I called my doctor

Health metrics are absolutely tarnished by a lack of proper context. Unsurprisingly, it turns out that you can't reliably take a concept as broad as health and reduce it to a number. We see the same arguments over and over with body fat percentages, vo2 max estimates, BMI, lactate thresholds, resting heart rate, HRV, and more. These are all useful metrics, but it's important to consider them in the proper context that each of them deserve.

This article gave an LLM a bunch of health metrics and then asked it to reduce it to a single score, didn't tell us any of the actual metric values, and then compared that to a doctor's opinion. Why anyone would expect these to align is beyond my understanding.

The most obvious thing that jumps out to me is that I've noticed doctors generally, for better or worse, consider "health" much differently than the fitness community does. It's different toolsets and different goals. If this person's VO2 max estimate was under 30, that's objectively a poor VO2 max by most standards, and an LLM trained on the internet's entire repository of fitness discussion is likely going to give this person a bad score in terms of cardio fitness. But a doctor who sees a person come in who isn't complaining about anything in particular, moves around fine, doesn't have risk factors like age or family history, and has good metrics on a blood test is probably going to say they're in fine cardio health regardless of what their wearable says.

I'd go so far to say this is probably the case for most people. Your average person is in really poor fitness-shape but just fine health-shape.

A year or so ago, I fed my wife's blood work results into chatgpt and it came back with a terrifying diagnosis. Even after a lot of back and forth it stuck to its guns. We went to a specialist who performed some additional tests and explained that the condition cannot be diagnosed with just the original blood work and said that she did not have the condition. The whole thing was a borderline traumatic ordeal that I'm still pretty pissed about.

LLMs are not a mythical universal machine learning model that you can feed any input and have it magically do the same thing a specialized ML model could do.

You can't feed an LLM years of time-series meteorological data, and expect it to work as a specialized weather model, you can't feed it years of medical time-series and expect it to work as a model specifically trained, and validated on this specific kind of data.

An LLM generates a stream of tokens. You feed it a giant set of CSVs, if it was not RL'd to do something useful with it, it will just try to make whatever sense of it and generate something that will most probably have no strong numerical relationship to your data, it will simulate an analysis, it won't do it.

You may have a giant context windows, but attention is sparse, the attention mechanism doesn't see your whole data at the same time, it can do some simple comparisons, like figuring out that if I say my current pressure is 210X180 I should call an ER immediately. But once I send it a time-series of my twice a day blood-pressure measurements for the last 10 years, it can't make any real sense of it.

Indeed, it would have been better for the author to ask the LLM to generate a python notebook to do some data analysis on it, and then run the notebook and share the result with the doctor.

Why do people even begin to believe that a large language model can usefully understand and interpret health data?

Sure, LLM companies and proponents bear responsibility for the positioning of LLM tools, and particularly their presentation as chat bots.

But from a systems point of view, it's hard to ignore the inequity and inconvenience of the US health system driving people to unrealistic alternatives.

(I wonder if anyone's gathering comparable stats on "Doctor LLM" interactions in different countries... there were some interesting ones that showed how "Doctor Google" was more of a problem in the US than elsewhere.)

A simple understanding of transformers should be enough to make someone see that using an LLM to analyze multi-variate time series data is a really stupid endeavor.

The problem is that false positives can be incredibly expensive in money, time, pain, and anxiety. Most people cannot afford (and healthcare system cannot handle) thousands of dollars in tests to disprove every AI hunch. And tests are rarely consequence free. This is effectively a negative externality of these AI health products and society is picking up the tab.

ChatGPT Health is a completely wreckless and dangerous product, they should be sued into oblivion for even naming it "health".

> Despite having access to my weight, blood pressure and cholesterol, ChatGPT based much of its negative assessment on an Apple Watch measurement known as VO2 max, the maximum amount of oxygen your body can consume during exercise. Apple says it collects an “estimate” of VO2 max, but the real thing requires a treadmill and a mask. Apple says its cardio fitness measures have been validated, but independent researchers have found those estimates can run low — by an average of 13 percent.

There's plenty of blame to go around for everyone, but at least for some of it (such as the above) I think the blame more rests on Apple for falsely representing the quality of their product (and TFA seems pretty clearly to be blasting OpenAI for this, not others like Apple).

What would you expect the behavior of the AI to be? Should it always assume bad data or potentially bad data? If so, that seems like it would defeat the point of having data at all as you could never draw any conclusions from it. Even disregarding statistical outliers, it's not at all clear what part of the data is "good" vs "unrealiable" especially when the company that collected that data claims that it's good data.

The author is a healthy person but the computer program still gave him a failing grade of F. It is irresponsible for these companies to release broken tools that can cause so much fear in real people. They are treating serious medical advice like it is just a video game or a toy. Real users should not be the ones testing these dangerous products.

We trained a foundation model specifically for wearable data: https://www.empirical.health/blog/wearable-foundation-model-...

The basic idea was to adapt JEPA (Yann LeCun's Joint-Embedding Predictive Architecture) to multivariate time series, in order to learn a latent space of human health from purely unlabeled data. Then, we tested the model using supervised fine tuning and evaluation on on a bunch of downstream tasks, such as predicting a diagnosis of hypertension (~87% accuracy). In theory, this model could be also aligned to the latent space of an LLM--similar to how CLIP aligns a vision model to an LLM.

IMO, this shows that accuracy in consumer health will require specialized models alongside standard LLMs.

This is not remotely surprising.

Look, AI Healthbros, I'll tell you quite clearly what I want from your statistical pattern analyzers, and you don't even have to pay me for the idea (though I wouldn't say no to a home or Enterprise IT gig at your startup):

I want an AI/ML tool to not merely analyze my medical info (ON DEVICE, no cloud sharing kthx), but also extrapolate patterns involving weather, location, screen time, and other "non-health" data.

Do I record taking tylenol when the barometric pressure drops? Start alerting me ahead of time so I can try to avoid a headache.

Does my screen time correlate to immediately decreased sleep scores? Send me a push notification or webhook I can act upon/script off of, like locking me out of my device for the night or dimming my lights.

Am I recording higher-intensity workouts in colder temperatures or inclement weather? Start tracking those metrics and maybe keep better track of balance readings during those events for improved mobility issue detection.

Got an app where I track cannabis use or alcohol consumption? Tie that to my mental health journal or biological readings to identify red flags or concerns about misuse.

Stop trying to replace people like my medical care team, and instead equip them with better insights and datasets they can more quickly act upon. "Subject has been reporting more negative moods in his mental health journal, an uptick in alcohol consumption above his baseline, and inconsistent cannabis use compared to prior patterns" equips the care team with a quick, verifiable blurb from larger datasets that can accelerate care and improve patient outcomes - without the hallucinations of generative AI.

I can't wait until it starts recommending signing me up for an OpenAI personalized multi-vitamin® supscription

Apple watch told me, based on vo2 max, that i'm almost dead, all the time. I went to the doctor, did a real test and it was complete nonsense. I had the watch replaced 3 times but same results, so I returned it and will not try again. Scaring people with stuff you cannot actually shut off (at least you couldn't before) is not great.

Original article can be read at https://www.washingtonpost.com/technology/2026/01/26/chatgpt....

Paywall-free version at https://archive.ph/k4Rxt

Typical Western coverage: “How dare they call me unhealthy.” In reality, the doctor said it needs further investigation and that some data isn’t great. They didn’t say “unhealthy”; they said “needs more investigation.” What’s wrong with that? Is the real issue just a bruised Western ego?