Founder of Apple’s FaceID and Vision Pro technologies have spent the past six years developing an artificial intelligence model that could one day help determine electrical activity in the brain to diagnose mental disorders.
Now, Gidi Littwin’s startup, Hemispheric, has raised $52 million in funding after collecting data on the brains of 100,000 people to train deep learning models to examine the brain without the need for invasive procedures.
Littwin left Apple in 2020, looking for a change. He found out when his Hemispheric co-founder Hagai Lalazar cold-messaged him on LinkedIn. Lalazar had begun developing artificial intelligence to study the brain without the need for surgery, and he was looking for a commercial innovator to drive the company forward. By the time he found Littwin, he had talked to about 75 people.
Littwin helped develop FaceID, and at the time was working on a hand-tracking augmented reality product, Vision Pro. As part of this, he had to collect what he told WIRED “hundreds of thousands worth of subject data” to train the deep learning models that power the technology.
“There was a lot of data collection behind these projects and we knew we had to build something similar in the Hemispheric,” Littwin said, “and we did.”
Because each person’s brain activity looks different, doctors have increasingly had to rely on subjective questionnaires and behavioral observations to diagnose depression, Alzheimer’s, and Parkinson’s. To address that, Littwin and Hagai collected their “most valuable asset:” a quarter of a million brain data from 100,000 paid volunteers across Asia, as well as in Tel Aviv, and Boston. Subjects performed a series of tasks that looked like games but activated different parts of their brains.
That data helped train the frontier model, which incorporates brain activity into electrical activity inside the skull in the same way that large linguistic models derive meaning through text analysis. They then tested the general model on small subsets of people, including those diagnosed with PTSD, schizophrenia, and depression and said the model made accurate deductions about the health of the human brain. The team is currently working on a clinical trial to test whether their model can diagnose and predict Alzheimer’s.
The team will submit its first product, which will be used to study PTSD, to the FDA for approval early next year. They hope that will allow them to release the product to the public later in 2027.
To help diagnose mental disorders, the patient wears a lightweight EEG headset that measures electrical activity in the brain for about 15 minutes while interacting with an app on a tablet. Hemispheric says its AI model will help doctors determine signals to make diagnoses, choose effective interventions by making predictions about treatment, and monitor progress.
“The future we envision is one where this is like a blood test,” Lalazar told WIRED in an interview. “The device will be very expensive, very inexpensive; it will be able to be sold and distributed to all psychiatric clinics, hospitals, and psychiatrists’ offices.”
AI-assisted diagnostic tools for conditions such as lung cancer are already in clinical use and speeding up access to treatment across Europe. Meanwhile, AI giants including OpenAI and Anthropic are expanding into healthcare, intensifying competition for a raft of startups in the space.
Hemispheric has raised early funding from investors including American and Israeli venture capital firms and individual investors, among them former Uber-backer Howard Morgan. They will use the money to develop relationships with governments, health organizations, and pharmaceutical companies, hire more in the US, and work toward regulatory approval. They also plan to measure more brain data from millions of people in an effort to improve their model
The pair are also developing their own brain scanners for information that the company believes can provide more useful data for their models than traditional EEGs. “These tools were never designed for machine learning and really not for deep learning,” Littwin said.
