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Medical Device

Multimodal AI for Brain Disorders

The diagnosis and care processes for brain conditions such as depression or dementia are complex, involve multiple tests, data types and specialists; and they are, in some cases, very dependent on the subjective ability of the patient to report on their condition. Some of these challenges have become at the same time an opportunity to improve the healthcare processes by leveraging AI.

Multimodal Machine Learning solutions can assist specialists and provide them with new tools and insights to make the healthcare pathway more efficient and objective. In this post, we introduce one of the projects we are developing at PickleTech focused on such improvements: HALO. Starting from a cohort of elite athletes, we leverage multimodal AI and the development of biomarkers, aiming to provide athletes and preparation teams with objective measures of their mental health.

Machine Learning for Medical Devices

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Medical devices containing a machine learning algorithmic component, e.g. for diagnosis, have a huge market and health potential. But getting the approval to sell a medical device is possible only after a thorough process of certification. For this, the particularities of statistical learning need to be taken into account as soon as possible in the device design. Otherwise, the risk of getting trapped in the certification process with suboptimal -viability killing- components is just too high. In this post we describe good practices for machine learning development and operationalization within medical devices, driven from our experience at PickleTech.

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