The company's next product will provide objective brain state data, much like a CGM provides constant glucose readings. This allows for data-driven mental health treatment, moving beyond subjective checklists and enabling closed-loop therapies with neuromodulators, fundamentally changing diagnostics and care.

The endgame for CZI's work is hyper-personalized, "N of one" medicine. Instead of the current empirical approach (e.g., trying different antidepressants for months), AI models will simulate an individual's unique biology to predict which specific therapy will work, eliminating guesswork and patient suffering.

The biggest limitation in precision medicine is the systemic failure to capture and learn from longitudinal data on how patients respond to treatments over time. Without this critical feedback loop, even the most sophisticated diagnostic models will fall short of their potential to improve care.

The primary challenge holding back precision medicine is not a lack of data or innovation. Instead, it's the operational difficulty of integrating and interpreting complex, siloed information quickly enough to make it clinically actionable for individual patients. The focus must shift from accumulation to execution.

The initial hurdle for precision psychiatry isn't achieving 100% accuracy. The goal is to be meaningfully better than the current trial-and-error standard. Moving the needle on treatment remission from 30% to 40-45% would be a huge clinical success, creating a new benchmark and starting a virtuous cycle of improvement.

Despite hype around genetics and multi-omics, their value in stratifying psychiatric patients is limited. Genetic variants have small effects, and peripheral samples like blood poorly reflect brain biology (e.g., blood serotonin comes from platelets, not the brain). Direct brain function measures are more reliable.

Traditional boundaries between neurology and psychiatry are artificial. From a brain circuit perspective, conditions overlap significantly—Parkinson's involves mood and cognition, and depression involves key neurological pathways. The focus should be on matching a measurable circuit dysfunction with a targeted therapy, regardless of the clinical specialty's historical label.

A Quantitative EEG (QEEG) or "brain map" analyzes brainwave patterns to identify cognitive struggles and even sleep quality. Practitioners can often describe a person's core challenges with surprising accuracy, providing objective data before any subjective report is given.

A patient's subjective report on their cognitive ability correlates more strongly with their overall mood than with objective cognitive test results. This disconnect reveals why objective measures like EEG or behavioral tests are essential; self-perception is an unreliable proxy for the underlying biological processes that need treatment.