Gidi Littwin, co-inventor of Apple’s FaceID and Vision Pro technology, has raised $52 million for his startup Hemispheric, which is developing an AI model designed to diagnose cognitive disorders by analyzing electrical activity in the brain. The funding was announced in 2026, six years after Littwin left Apple to co-found the company.
Hemispheric was co-founded with neuroscientist Hagai Lalazar, who had been working on non-invasive AI-based brain analysis and approached Littwin via LinkedIn after speaking with around 75 other candidates. The company has since collected a quarter of a million hours of brain data from 100,000 paid volunteers across Asia, Tel Aviv, and Boston to train its deep-learning models.
To use the system, a patient wears a lightweight EEG headset for roughly 15 minutes while interacting with a tablet app. Hemispheric’s AI model then helps clinicians interpret the signals to support diagnosis, predict treatment outcomes, and monitor patient progress. The company is targeting conditions including PTSD, depression, schizophrenia, Alzheimer’s, and Parkinson’s disease.
Littwin and Lalazar plan to submit their first product — focused on PTSD — to the FDA for approval in early 2027, with a public rollout targeted for later that year. The team is also conducting a clinical study to test whether the model can diagnose and predict Alzheimer’s.
The $52 million raise came from American and Israeli venture capital firms and individual investors, including early Uber-backer Howard Morgan. Hemispheric says the funds will support government and healthcare partnerships, US hiring, regulatory efforts, and expanded data collection from millions more subjects.
“The future that we envision is one where this is akin to a blood test,” Lalazar said. “The device is going to be very, very cheap; it will be able to be sold and distributed throughout mental health clinics, hospitals, and even psychologists’ offices.”
Hemispheric is also developing its own brain scanning hardware, which Littwin says could provide more useful data for machine learning than traditional EEG devices.
Source: WIRED