# WangLab at MEDIQA-Chat 2023: Clinical Note Generation from Doctor-Patient Conversations using Large Language Models

arXiv: https://arxiv.org/abs/2305.02220 · HTML: https://mlsanity.com/paper/2305.02220/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 36 references that are in the map.
- First posted on arXiv 3 May 2023.

## Builds on (most cited first, 20 of 20)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [Longformer: The Long-Document Transformer](https://mlsanity.com/paper/2004.05150/) (2020)
- [Scaling Instruction-Finetuned Language Models](https://mlsanity.com/paper/2210.11416/) (2022)
- [BLEURT: Learning Robust Metrics for Text Generation](https://mlsanity.com/paper/2004.04696/) (2020)
- [Aligning Books and Movies: Towards Story-like Visual Explanations by Watching Movies and Reading Books](https://mlsanity.com/paper/1506.06724/) (2015)
- [A Discourse-Aware Attention Model for Abstractive Summarization of Long Documents](https://mlsanity.com/paper/1804.05685/) (2018)
- [One Embedder, Any Task: Instruction-Finetuned Text Embeddings](https://mlsanity.com/paper/2212.09741/) (2022)
- [Learning Compact Metrics for MT](https://mlsanity.com/paper/2110.06341/) (2021)
- [Medically Aware GPT-3 as a Data Generator for Medical Dialogue Summarization](https://mlsanity.com/paper/2110.07356/) (2021)
- [Generating SOAP Notes from Doctor-Patient Conversations Using Modular Summarization Techniques](https://mlsanity.com/paper/2005.01795/) (2020)
- [Dr. Summarize: Global Summarization of Medical Dialogue by Exploiting Local Structures](https://mlsanity.com/paper/2009.08666/) (2020)
- [Leveraging Pretrained Models for Automatic Summarization of Doctor-Patient Conversations](https://mlsanity.com/paper/2109.12174/) (2021)
- [Re-Examining System-Level Correlations of Automatic Summarization Evaluation Metrics](https://mlsanity.com/paper/2204.10216/) (2022)
- [Human Evaluation and Correlation with Automatic Metrics in Consultation Note Generation](https://mlsanity.com/paper/2204.00447/) (2022)
- [PriMock57: A Dataset Of Primary Care Mock Consultations](https://mlsanity.com/paper/2204.00333/) (2022)
- [User-Driven Research of Medical Note Generation Software](https://mlsanity.com/paper/2205.02549/) (2022)
- [Consultation Checklists: Standardising the Human Evaluation of Medical Note Generation](https://mlsanity.com/paper/2211.09455/) (2022)

## Built on by (most cited first, 7 of 7)

- [Large language models in healthcare and medical domain: A review](https://mlsanity.com/paper/2401.06775/) (2023)
- [A Survey of GPT-3 Family Large Language Models Including ChatGPT and GPT-4](https://mlsanity.com/paper/2310.12321/) (2023)
- [CLINICSUM: Utilizing Language Models for Generating Clinical Summaries from Patient-Doctor Conversations](https://mlsanity.com/paper/2412.04254/) (2024)
- [Automated Survey Collection with LLM-based Conversational Agents](https://mlsanity.com/paper/2504.02891/) (2025)
- [WangLab at MEDIQA-CORR 2024: Optimized LLM-based Programs for Medical Error Detection and Correction](https://mlsanity.com/paper/2404.14544/) (2024)
- [Are LLMs reliable? An exploration of the reliability of large language models in clinical note generation](https://mlsanity.com/paper/2505.17095/) (2025)
- [DocTalkBN: A Novel Dataset of Expert Telemedicine Conversations in Bengali](https://mlsanity.com/paper/2608.27110/) (2026)

## Abstract (arXiv)

This paper describes our submission to the MEDIQA-Chat 2023 shared task for automatic clinical note generation from doctor-patient conversations. We report results for two approaches: the first fine-tunes a pre-trained language model (PLM) on the shared task data, and the second uses few-shot in-context learning (ICL) with a large language model (LLM). Both achieve high performance as measured by automatic metrics (e.g. ROUGE, BERTScore) and ranked second and first, respectively, of all submissions to the shared task. Expert human scrutiny indicates that notes generated via the ICL-based approach with GPT-4 are preferred about as often as human-written notes, making it a promising path toward automated note generation from doctor-patient conversations.