Responsible AI. Life changing data. Accessible technology.

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Empowering the Healthcare Community to Do More Sooner for Children and Their Families

At Cognoa, we use responsible AI and a whole child data approach to better understand, track and treat children with behavioral health conditions. Smart, accessible, equitable diagnostics and therapeutics in the hands of the healthcare community support life-changing early action.

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What responsible AI means to us:

Responsible Al is objective, representative, equitable.

AI systems are only as good as the data they’re built upon. That’s why at Cognoa, we actively seek diverse data and train our AI to represent and account for differences in gender, race, ethnicity, and socio-economic backgrounds, recognizing, for example, that girls with autism show different traits than boys.

Responsible Al works the same across race and gender.

Equitable AI is critical because current diagnostic processes are failing many children and families. Girls are diagnosed at least 1.5 years later than boys. One in four children under the age of 8 living with autism, most of them black or Hispanic, are not being diagnosed at all. This is due to the lack of access in our system and because diagnosis has historically been skewed towards autism traits prevalent in white boys, who present characteristics of autism differently than girls and children who are non-white

Responsible Al continues to learn and develop.

AI can learn and improve. We evaluate and monitor our AI’s performance and impact, and we ensure our AI-based products are safe and representative of diverse populations. We promise to work with FDA when there are opportunities to improve the performance, safety, and the inclusivity of our device.

Responsible Al is faster and smarter.

Using AI and an accessible, digital approach, providers can reduce time to diagnosis and life-changing early interventions faster and smarter than ever before.

Responsible Al does not replace; it augments and expands ability.

We have no goal to replace clinical judgement. Our goal is to fortify clinical judgement and speed accurate decision-making with computational analysis of mass, rich, unbiased data, that even the brightest minds with the most experience can simply not draw from.

Responsible Al provides access that creates action.

Traditional diagnostic surveys for autism and other behavioral conditions can be prohibitive for families because they are lengthy and must be administered by a trained, licensed clinician, in person, in environments that are unfamiliar to the child. This is hard on kids and families, especially for those living in rural areas, and especially for working moms. More than 80% of counties in the U.S. do not have diagnostic centers for autism.

Responsible Al is mobile to meet patients where they are.

Delivered through a ubiquitous device, the smartphone, Canvas Dx makes it possible to collect data that is, in prevalent diagnostic processes, difficult or even impossible to obtain, such as videos of a child’s natural behavior at home.

Responsible Al confidently says, “I don’t know.”

Canvas Dx deliberately abstains from making a diagnostic prediction when there is insufficient information to confidently make a highly accurate positive or negative prediction. This is an important safety mechanism to reduce the risk of false classifications. This means that providers can have a high level of confidence in the accuracy of a positive or negative result from Canvas Dx.

Our whole child data approach facilitates a robust pipeline for pediatric developmental & behavioral health.

Cognoa enables the collection and analysis of multidimensional features of a child including social, behavioral, executive functioning, and language from both caregivers and clinicians.

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Whole child data drives innovations that enable evaluation across multiple indications and tracking of a child’s developmental progress.whole child data

High dimensional characterization of whole child data further expands our product pipeline to include accessible therapies.

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