An advanced trend in Healthcare IT is integration of ML & AI into assessment tools & diagnostic devices. Possibilities of enhancing the precision, effectiveness and individuality of medical diagnostics with the usage of these AI/ML components. However, they have their special difficulties as far as validation and regulation is concerned, and are also inherent when implementing these specifications. Before these gadgets can be relied upon to diagnose without endangering the health of patients, the AI/ML parts of the gadgets have to be certified. One observes that while comparing with traditional software applications, AI/ML models are more evolvable and can improve with time, maybe with a change in their characteristics. Due to this dynamic nature of procedure, there is a need for severe and constant validation processes.The process of checking the train and test data sets for their ethical as well as their representativeness and quality is called data validation. This work aims at exploring several strategies for validation, problems encountered while applying them, and the implications on reliability and certification of a diagnostic equipment with AI/ML elements integrated into it.

An advanced trend in Healthcare IT is integration of ML & AI into assessment tools & diagnostic devices.