Regulatory affairs, which sits at the intersection of medicine and law, can benefit significantly from AI-enabled automation. Classification task is the initial step in which manufacturers position their products to regulatory authorities, and it plays a critical role in determining market access, regulatory scrutiny, and ultimately, patient safety. In this study, we investigate a broad range of AI models -- including traditional machine learning (ML) algorithms, deep learning architectures, and large language models -- using a regulatory dataset of medical device descriptions. We evaluate each model along three key dimensions: accuracy, interpretability, and computational cost.

Hospitals are on the radar of hackers as "soft" and valuable targets. The modern medical facility is connected to the internet in a multitude of ways. These connections include email clients, multi-location data integration systems, medical devices, and off-premise vendor support; all which leave hospitals and clinical networks extremely vulnerable to attack.