In the United States, medical devices are regulated and subject to review by the Food and Drug Administration (“FDA”) before they can be marketed. Novel medical devices have to undergo a premarket authorization (PMA) process before they can proceed to market, and this process can be fairly cumbersome, expensive, and time-consuming. An alternate faster and less-pricey pathway to going to market is the 510(k) pathway. In this approach, if the device manufacturer can show that their device is substantially equivalent in safety and effectiveness to a pre-existing FDA-approved marketed device (or “predicates”), they can go to market with their device. Due to the possibility of daisy-chaining predicate devices, it can very quickly be difficult to unravel the logic and justification of how a particular medical device’s equivalence was established. From patients’ perspective, this minimizes transparency in the process. From a vendor perspective, it can be difficult to determine the right predicate that applies to their device. PrediGen (short for “Predicates Genealogy”) is being developed as an approach to graphically map the connectivity of various predicates in the medical device field using data publicly made available by the FDA, and by combining text mining and novel natural language processing (NLP) techniques. Besides enabling a better understanding of the risks and benefits of the 510(k) process, this tool can increase consumer confidence in the medical devices that are currently in the marketplace.