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Silo.AI Blog 12.5.2020


Petteri, you are responsible for clients in the medical and healthcare domain at Silo.AI. Could you elaborate on some AI use cases where you’ve seen a lot of value being created in these fields?

I’d say that we have two types of projects that we’ve seen tremendous value being created through the usage of artificial intelligence (AI).

The first one is applying computer vision algorithms to medical imaging, such as X-ray or CT images to spot anomalies such as bone fractures or cancer cells. With sufficient training data, customized algorithms and effective user interfaces, AI can be a great asset to doctors who typically spend significant amounts of time scrutinizing the imaging and planning operations. If that time can be reduced by AI preprocessing and making suggestions, the doctor will have more time available to actually focus on treating the patient and we’ll improve the hospital efficiency.

Secondly, the medical field has moved from acknowledging and treating medical conditions to forecasting the situation based on machine learning algorithms that process the patient data. We’ve worked with patient vital signs analysis and anomalies prediction, and there’s a huge potential for improving the quality of life of the patients as well as saving time and resources with predictive treatment. The similar theme of AI-driven predictive maintenance has been introduced to industrial settings already some time ago, but it is now finally being adopted by the medical and healthcare industry.

Your background is in human-centered strategy and user-experience. What are some of the best practices about how you have seen AI supporting healthcare workers while keeping the patient in focus?

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