  This paper proposes a possible method using natural language processing that
might assist in the FDA medical device marketing process. Actual device
descriptions are taken and matched with the device description in FDA Title 21
of CFR to determine their corresponding device type. Both pre-trained word
embeddings such as FastText and large pre-trained sentence embedding models
such as sentence transformers are evaluated on their accuracy in characterizing
a piece of device description. An experiment is also done to test whether these
models can identify the devices wrongly classified in the FDA database. The
result shows that sentence transformer with T5 and MPNet and GPT-3 semantic
search embedding show high accuracy in identifying the correct classification
by narrowing down the correct label to be contained in the first 15 most likely
results, as compared to 2585 types of device descriptions that must be manually
searched through. On the other hand, all methods demonstrate high accuracy in
identifying completely incorrectly labeled devices, but all fail to identify
false device classifications that are wrong but closely related to the true
label.
