Aiberry secures $8 million in seed funding

AI-powered mental health screening company Aiberry has secured $8 million (£6.7 million) in seed funding.

The new funds, which bring total funding to $10 million (£8.5 million), will be used to accelerate adoption of the Aiberry platform.

The platform uses an AI-powered therapeutic assistant to conduct a conversation to detect mental health disorders by analysing what is being said, the speech patterns being used and subtle changes in facial expressions.

Aiberry co-CEO, Linda Chung, said:

“Early identification and prevention have long been a challenge when it comes to mental health in the U.S.

“While many platforms have been developed to provide services ranging from preventative care to self-care and even clinical care, the intake process and progress monitoring has failed to advance.

“Aiberry is an ideal partner for existing digital health platforms and corporate wellness programmes, especially in an era where people are becoming increasingly knowledgeable and proactive when it comes to their mental health and wellness.”

Aiberry’s platform enables screenings in a wide range of settings, including doctors’ offices, schools, workplaces and homes.

At the end of each screening, users and clinicians are provided with a risk level score for mental health disorders including suicidal ideation alongside screening insights including mood, concentration and energy levels.

The platform is based on decades of research by Aiberry c-founder and chief scientist, Dr Newton Howard, a leading brain and cognitive scientist and a professor at Georgetown University and Oxford University.

Lynne Dunbrack, Group Vice President for investor, IDC Health Insights, added

“AI platforms such as Aiberry provide a wide range of benefits compared to traditional diagnostic methods.

They eliminate the tedious process of filling out lengthy intake forms that fail to provide a comprehensive understanding of the patient’s situation.

“Furthermore, these platforms offer objective progress tracking, reducing the impact of human bias and increasing the reliability and accuracy of results.”