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.
In Emergency Medicine, decisions need to be made fast, and with a limited view on the patient’s health status. While the time urgency is imperative, in Emergency Medicine not all the decision outcomes are visible immediately. Actually, some may take quite some time to show up, turning feedback and learning processes into a challenge. Limited resources impose in addition a very severe constraint, which makes Emergency Medicine too many times a complex resource optimization problem in nature. We discuss here how Data Science has the potential to improve Emergency Medicine, focusing on some of the opportunities for such a complex context.