Advancing ADHD diagnosis: How Qbtech built a mobile AI assessment Model Using Amazon SageMaker AI

Why it matters (2 min)

• This post is cowritten with Dr.
• Mikkel Hansen from Qbtech.
• Open receipts to verify and go deeper.

Go deeper (8 min)

Context

This post is cowritten with Dr. Mikkel Hansen from Qbtech. The assessment and diagnosis of attention deficit hyperactive disorder (ADHD) has traditionally relied on clinical observations and behavioral evaluations. While these methods are valuable, the process can be complex and time-intensive. Qbtech , founded in 2002 in Stockholm, Sweden, enhances ADHD diagnosis by integrating objective measurements with clinical expertise, helping clinicians make more informed diagnostic decisions. With over one million tests completed across 14 countries, the company’s FDA-cleared and CE-marked products—QbTest (clinic-based) and QbCheck (remote)— have established themselves as widely-adopted tools for objective ADHD testing. Now, Qbtech aims at extending their capabilities with QbMobile, a smartphone-native assessment that uses Amazon Web Services (AWS) to bring clinical-grade ADHD testing directly to patients’ devices. In this post, we explore how Qbtech streamlined their machine learning (ML) workflow using Amazon SageMaker AI , a fully managed service to build, train and deploy ML models, and AWS Glue , a serverless service that makes data integration simpler, faster, and more cost…

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Receipts

1. Advancing ADHD diagnosis: How Qbtech built a mobile AI assessment Model Using Amazon SageMaker AI (AWS Machine Learning Blog)