Show Notes
Thirteen days from now, the FDA convenes a panel that has never convened before.
On September 23, 2026, the Molecular and Clinical Genetics Panel sits down to vote on Grail's Galleri test, the first multi-cancer early detection test ever to reach an FDA advisory committee. Every founder building in early detection is watching. Shlomi Madar is watching too. He just is not building the same thing.
Madar is the CEO of SpotitEarly, an Englewood, New Jersey company founded in 2020 that screens for four cancers from a single breath sample collected at your kitchen table. Grail reads methylation patterns in cell-free DNA. SpotitEarly reads a mask you breathed into for a few minutes. And the sensor at the front of its pipeline is not a machine. It is a beagle.
This is the part where most people assume it is a gimmick. It is not. Madar has a PhD in cancer research from the Weizmann Institute of Science, and the reason the company landed on dogs is boring and rigorous: nothing built by humans comes close. The entire field of electronic noses, mass spectrometry included, keeps running into the same wall, which is sensitivity threshold. A dog detects at parts per trillion. Madar's favorite way to explain it: fill two Olympic swimming pools, stir half a teaspoon of sugar into one, and a dog will tell you which pool you tried to trick him with.
The problem with a dog is not the nose. It is that a dog cannot fill out a lab report. That is the entire engineering problem SpotitEarly set out to solve, and it is the reason this episode belongs on a founder podcast rather than a science one. Madar's team spent five and a half years turning an animal's opinion into a reproducible, scalable, auditable data output.
Here is how the machine actually works. You breathe into a surgical mask, seal it, and mail it. At the lab, the mask goes into a sniffing port, a cone shaped device where the dogs interact with the scent. A camera watches from above. A second small camera inside the port captures facial gestures. A microphone captures breathing patterns. The dogs wear vests with accelerometers tracking velocity. Each sensor feeds its own trained machine learning model. Video gets broken into frames, frames into pixels, and the models hunt for deviation from a database that already associates canine behavior with positive and negative samples. Multiple dogs sniff each sample. Each dog sniffs more than once. Thousands of data points per sample, and the AI layer throws out anything that deviates from the norm.
The company calls the platform LUCID. The Rainbow Study, run with medical centers in Israel including Sourasky, Hadassah, and Rambam, put 1,386 participants through it under double-blind conditions and reported 93.9% sensitivity and 94.3% specificity. Per cancer sensitivity came in at 97% for lung, 97% for prostate, 94% for breast, and 86% for colorectal. At stages zero to two, the earliest ones, performance held. The paper ran in Scientific Reports.
Madar is careful, repeatedly and unprompted, about what the test is. It is a triage layer. A positive signal does not mean skip the mammogram. It means go do the whole standard of care gamut, and now you have two orthogonal methods pointing at the same thing, which is exactly what you want in screening.
SpotitEarly: https://www.spotitearly.com
Shlomi Madar on LinkedIn: https://www.linkedin.com/in/shlomimadar/
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