# Evaluating Embeddings for One-Shot Classification of Doctor-AI Consultations

arXiv: https://arxiv.org/abs/2402.04442 · HTML: https://mlsanity.com/paper/2402.04442/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 32 references that are in the map.
- First posted on arXiv 6 Feb 2024.

## Builds on (most cited first, 8 of 8)

- [DISC-MedLLM: Bridging General Large Language Models and Real-World Medical Consultation](https://mlsanity.com/paper/2308.14346/) (2023)
- [Mask-guided BERT for Few Shot Text Classification](https://mlsanity.com/paper/2302.10447/) (2023)
- [Causality-Driven One-Shot Learning for Prostate Cancer Grading from MRI](https://mlsanity.com/paper/2309.10725/) (2023)
- [Legend at ArAIEval Shared Task: Persuasion Technique Detection using a Language-Agnostic Text Representation Model](https://mlsanity.com/paper/2310.09661/) (2023)
- [Transformer-based approaches to Sentiment Detection](https://mlsanity.com/paper/2303.07292/) (2023)
- [MedAI Dialog Corpus (MEDIC): Zero-Shot Classification of Doctor and AI Responses in Health Consultations](https://mlsanity.com/paper/2310.12489/) (2023)
- [Decoding Virtual Healthcare Success through Knowledge-Aware and Multimodal Predictive Modeling](https://mlsanity.com/paper/2306.03833/) (2023)
- [Position and Orientation-Aware One-Shot Learning for Medical Action Recognition from Signal Data](https://mlsanity.com/paper/2309.15635/) (2023)

## Abstract (arXiv)

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.