Framework for Integration Decentralized and Untrusted Multi-Vendor IoMT Environments

Lack of standardization is highly visible while we use historical data sets or compare our model with others that use IoMT devices from different vendors. The problem also concerns the trust in highly decentralized and anonymous environments where sensitive data are transferred through the Internet and then are analyzed by third-party companies. In our research we propose a standard that has been implemented in the form of framework that allows describing requirements for methods and platforms that collect, manage, share, and perform data analysis form the Internet of Medical Things in order to increase trust. Further, we can distinguish two types of IoMT devices: passive and active. Passive devices measure some parameters of the body and save them in databases. Active devices have the functionality of passive devices and moreover, they can act in a defined way, eg.: inject directly into the patient's body some elements such as a medicament, electric signals to the nervous system, stimulus pacemaker, etc. Nevertheless how to create a safe and transparent environment for using data active sensors, developing safe ML models, performing medical decisions based on the created models and finally deploy this decision to the specified device. While the IoMT devices are used in real-life, professional healthcare the control system should offer tools for backtracking decisions, allowing e.g. to find who made a mistake, or which event caused a particular decision. Our framework provides backtracking in the IoMT environment in which for each medical decision supported by ML models we can prove which sensor sends the data, which data was used to create prediction/recommendation, what prediction was produced, who and when use it, what medical decision was made by who. We propose a vendor transparency framework for each IoMT devices and ML models that will process the medical data in order to increase patient's privacy and prevent for eventual data leaking.