Effective communication between healthcare providers and patients is crucial
to providing high-quality patient care. In this work, we investigate how
Doctor-written and AI-generated texts in healthcare consultations can be
classified using state-of-the-art embeddings and one-shot classification
systems. By analyzing embeddings such as bag-of-words, character n-grams,
Word2Vec, GloVe, fastText, and GPT2 embeddings, we examine how well our
one-shot classification systems capture semantic information within medical
consultations. Results show that the embeddings are capable of capturing
semantic features from text in a reliable and adaptable manner. Overall,
Word2Vec, GloVe and Character n-grams embeddings performed well, indicating
their suitability for modeling targeted to this task. GPT2 embedding also shows
notable performance, indicating its suitability for models tailored to this
task as well. Our machine learning architectures significantly improved the
quality of health conversations when training data are scarce, improving
communication between patients and healthcare providers.

医疗协商中的文本如何使用最先进的嵌入和一次性分类系统进行分类，分析表明，嵌入能够可靠并且适应地捕捉文本的语义特征。Word2Vec、GloVe 和字符 n-gram 嵌入表现良好，GPT2 嵌入也表现出显著性能，我们的机器学习架构在数据稀缺的情况下显著改善了患者和医疗提供者之间的交流质量。