AI Diagnoses Defects in Medical Devices

Bad hardware components lie behind many failures in medical devices, according to Oshri Cohen, CEO of Cybord. The devices contain tens of computer chips and other components, so measuring quality becomes a tedious process. Compounding the difficulty of measuring quality is the constant churn of components. Take personal computers. Two generic laptops sitting on the shelf from one of the big manufacturers could have very different components, because these manufacturers diligently depress costs by seeking the cheapest components from month to month. Medical devices, while more high-end, also mix and match. They also suffer the risk of getting knock-off components from unethical vendors simulating more high-quality components. Cybord has found a way to use cameras and AI-driven analysis to automate quality checks. A manufacturer can run each device through Cybord’s system, which in about one minute can scan all the components and return a quality report. The report shows each component in use with meta-information such as the manufacturing date. Sometimes, the component is not what its label claims it is, a clear problem. Damage or poor quality can also be identified by running the image of each component through the AI-generated model. Examples of damage the model can detect include defects in the component or how it is set into the device, as well as corrosion, oxidation, and mold (Figure 1). Of course, the model can also complain if the image is too blurry to analyze. ...
Source: EMR and HIPAA - Category: Information Technology Authors: Tags: AI/Machine Learning Analytics/Big Data Health IT Company Healthcare IT Cybord Healthcare AI Healthcare Manufacturing Medical Device Manufacturing medical device safety Medical Devices Oshri Cohen Source Type: blogs