What is Ground Truth?
Before you believe an AI claim in health, check it here.
Every hype claim is a true sentence with the conditions cut out. We put the cut part back, trace it to the primary source, and mark the hidden part in red.
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For people deciding what health-AI evidence deserves to be reported, funded, regulated, or deployed.
A health-AI headline, as published
The cut part, put back
Latest
Featured analysis
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The AI eye exam can read the retina. Can it save sight?
Retinal AI can identify referable diabetic retinopathy quickly and accurately. Ground Truth traced 29 studies and 15 public claims through the care chain. Across 17 selected patient-count pathways, referral gains depended on the surrounding workflow; only two separately counted who needed treatment, and none reported completed treatment or a longitudinal vision-outcome denominator.
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Health records, then AI: ask what actually moved
Almost every health-technology claim rests on something that moved, and the trick is knowing what.
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“Predicts risk of more than 300 diseases”: what the Aladynoulli paper reports, and what peer review narrowed
Harvard Medical School says a new AI tool predicts the risk of more than 300 diseases from existing patient data.
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2.9% or 8%? We recomputed the benchmark behind three “#1” claims
Three clinical-AI companies announced first place from the same Stanford–Harvard clinical-safety preprint in thirteen days.
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“More accurate” than clinicians: what Google’s SymptomAI was actually compared against
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How to read an AI scale-up proposal
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How to read a “99% accurate” diagnostic-AI claim
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The NHS cancer blood test was “99% accurate.” Which 99%?
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They want a doctor in every pocket. We handed the pocket a lethal order.
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AI beat the doctors. So we regraded the doctors.
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What is “ground truth” in health AI?
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Google made Gemma “medical.” We gave it the job.
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“As accurate as a sonographer”: what blind-sweep ultrasound AI actually proved
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“On par with nurses”: what Hippocratic AI’s headline number actually measured
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“Medical superintelligence”: what Microsoft’s 85.5% actually beat
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The scoreboard that can’t be drawn: what 15 “AI-beats-clinician” health studies actually measured
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How to read an “AI beats doctors” claim
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“16% fewer errors”: what the OpenAI–Penda Health study actually measured
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How to read a health-chatbot impact claim
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How to read an African-language AI benchmark without getting fooled
The editorial standard
Credibility you can inspect.
Traced to source
Every claim we examine is linked to the primary material — the paper, model card, or registry — so you can check it yourself.
Independent by design
We take no money from, and hold no affiliation with, the companies or funders whose claims we scrutinize.
Corrected in public
When we get something wrong, we say so on the page, with the date and what changed. Corrections are a feature, not an embarrassment.
The newsletter
Get the hidden conditions behind the headline.
Every few weeks: one big health-AI claim, traced to its primary source, with the part the headline left out put back in red. That’s the whole email — one-click unsubscribe, no tracking pixels.