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.

翻译：医疗提供者与患者之间的有效沟通对确保高质量患者护理至关重要。本研究探索如何利用最先进的嵌入表示与单样本分类系统，对医疗咨询中医生撰写的文本及AI生成的文本进行分类。通过分析词袋模型、字符n-gram、Word2Vec、GloVe、fastText及GPT2嵌入表示，我们评估了单样本分类系统在医疗咨询中捕捉语义信息的能力。结果表明，这些嵌入表示能够以可靠且可适配的方式提取文本中的语义特征。总体而言，Word2Vec、GloVe及字符n-gram嵌入表现优异，表明其适用于该任务的建模需求。GPT2嵌入同样展现了显著性能，进一步验证其对该任务导向模型的适配性。我们的机器学习架构在训练数据稀缺时显著提升了健康对话质量，从而改善了患者与医疗提供者之间的沟通效率。