Deliberate AI has announced that its depression and anxiety model, the artificial intelligence (AI)-generated Clinical Outcome Assessment (AI-COA) has been accepted under the US Food and Drug Administration’s (FDA) Innovative Science and Technology Approaches for New Drugs (ISTAND) Pilot Program.
The tool uses advanced multimodal behavioural signal processing and machine learning (ML) to collect mental health symptoms and offers a consistent, bias-free assessment method.
The AI-COA model was accepted by the FDA’s Center for Drug Evaluation and Research (CDER) and Center for Biologics Evaluation and Research (CBER).
It is said to be the first AI-enabled and digital health technology-based initiative and the first psychiatry and neuroscience project in ISTAND.
Under the programme, the depression and anxiety model will get support to become a qualified drug development tool (DDT).
Deliberate AI founder and CEO Marc Aafjes said: “The FDA’s recognition – including Deliberate’s AI-COA in this programme – heralds a new epoch in developing and monitoring treatments for Depression and Anxiety.
“Together, we can pave the way for more powerful, efficient, and faster clinical trials, addressing a critical barrier in psychiatric drug development, ultimately leading to enhanced patient care and a profound public health impact.”
Introduced in 2020, ISTAND assists in the development of new DDTs for use in regulatory applications for new medical products.
The submission of AI-COA’s approved letter of intent (LOI) is for measuring the severity of anxiety and depression that uses a variety of behavioural and physiological indices of depression in an ML model.
In clinical studies, the assessment tool used its AI system to supplement human clinical assessments to increase accuracy and dependability, the digital health technology company said.
Along with clinical trials, Deliberate AI is also working on improving AI-COA for use in clinical encounters and psychotherapy. This will allow more accurate triage and substitute patient surveys with passive background monitoring for improved measurement-based care.
