  Zero-shot classification enables text to be classified into classes not seen
during training. In this study, we examine the efficacy of zero-shot learning
models in classifying healthcare consultation responses from Doctors and AI
systems. The models evaluated include BART, BERT, XLM, XLM-R and DistilBERT.
The models were tested on three different datasets based on a binary and
multi-label analysis to identify the origins of text in health consultations
without any prior corpus training. According to our findings, the zero-shot
language models show a good understanding of language generally, but has
limitations when trying to classify doctor and AI responses to healthcare
consultations. This research provides a foundation for future research in the
field of medical text classification by informing the development of more
accurate methods of classifying text written by Doctors and AI systems in
health consultations.
