# MedAI Dialog Corpus (MEDIC): Zero-Shot Classification of Doctor and AI Responses in Health Consultations

arXiv: https://arxiv.org/abs/2310.12489 · HTML: https://mlsanity.com/paper/2310.12489/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 39 references that are in the map.
- First posted on arXiv 19 Oct 2023.

## Builds on (most cited first, 11 of 11)

- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://mlsanity.com/paper/1910.01108/) (2019)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [Cross-lingual Language Model Pretraining](https://mlsanity.com/paper/1901.07291/) (2019)
- [DeID-GPT: Zero-shot Medical Text De-Identification by GPT-4](https://mlsanity.com/paper/2303.11032/) (2023)
- [ChatGPT or Human? Detect and Explain. Explaining Decisions of Machine Learning Model for Detecting Short ChatGPT-generated Text](https://mlsanity.com/paper/2301.13852/) (2023)
- [CPLLM: Clinical Prediction with Large Language Models](https://mlsanity.com/paper/2309.11295/) (2023)
- [Comparative Analysis of Text Classification Approaches in Electronic Health Records](https://mlsanity.com/paper/2005.06624/) (2020)
- [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)
- [Zero-Shot and Few-Shot Classification of Biomedical Articles in Context of the COVID-19 Pandemic](https://mlsanity.com/paper/2201.03017/) (2022)

## Built on by (most cited first, 2 of 2)

- [MedConceptsQA: Open Source Medical Concepts QA Benchmark](https://mlsanity.com/paper/2405.07348/) (2024)
- [Evaluating Embeddings for One-Shot Classification of Doctor-AI Consultations](https://mlsanity.com/paper/2402.04442/) (2024)

## Abstract (arXiv)

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.