# User-Driven Research of Medical Note Generation Software

arXiv: https://arxiv.org/abs/2205.02549 · HTML: https://mlsanity.com/paper/2205.02549/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 28 references that are in the map.
- First posted on arXiv 5 May 2022.

## Builds on (most cited first, 10 of 10)

- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [Abstractive Text Summarization Using Sequence-to-Sequence RNNs and Beyond](https://mlsanity.com/paper/1602.06023/) (2016)
- [Medically Aware GPT-3 as a Data Generator for Medical Dialogue Summarization](https://mlsanity.com/paper/2110.07356/) (2021)
- [Learning to Summarize Radiology Findings](https://mlsanity.com/paper/1809.04698/) (2018)
- [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)
- [Ontology-Aware Clinical Abstractive Summarization](https://mlsanity.com/paper/1905.05818/) (2019)
- [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)
- [A preliminary study on evaluating Consultation Notes with Post-Editing](https://mlsanity.com/paper/2104.04402/) (2021)

## Built on by (most cited first, 13 of 13)

- [Summaries, Highlights, and Action items: Design, implementation and evaluation of an LLM-powered meeting recap system](https://mlsanity.com/paper/2307.15793/) (2023)
- [An Investigation of Evaluation Metrics for Automated Medical Note Generation](https://mlsanity.com/paper/2305.17364/) (2023)
- [SPHERE: An Evaluation Card for Human-AI Systems](https://mlsanity.com/paper/2504.07971/) (2025)
- [Consultation Checklists: Standardising the Human Evaluation of Medical Note Generation](https://mlsanity.com/paper/2211.09455/) (2022)
- [Informing clinical assessment by contextualizing post-hoc explanations of risk prediction models in type-2 diabetes](https://mlsanity.com/paper/2302.05752/) (2023)
- [WangLab at MEDIQA-Chat 2023: Clinical Note Generation from Doctor-Patient Conversations using Large Language Models](https://mlsanity.com/paper/2305.02220/) (2023)
- [Improving Clinical Note Generation from Complex Doctor-Patient Conversation](https://mlsanity.com/paper/2408.14568/) (2024)
- [CLINICSUM: Utilizing Language Models for Generating Clinical Summaries from Patient-Doctor Conversations](https://mlsanity.com/paper/2412.04254/) (2024)
- [LLMs for Doctors: Leveraging Medical LLMs to Assist Doctors, Not Replace Them](https://mlsanity.com/paper/2406.18034/) (2024)
- [Explicit and Implicit Semantic Ranking Framework](https://mlsanity.com/paper/2304.04918/) (2023)
- [Effectiveness of ChatGPT in explaining complex medical reports to patients](https://mlsanity.com/paper/2406.15963/) (2024)
- [RxSafeBench: Identifying Medication Safety Issues of Large Language Models in Simulated Consultation](https://mlsanity.com/paper/2511.04328/) (2025)
- [Conversational Assistants to support Heart Failure Patients: comparing a Neurosymbolic Architecture with ChatGPT](https://mlsanity.com/paper/2504.17753/) (2025)

## Abstract (arXiv)

A growing body of work uses Natural Language Processing (NLP) methods to automatically generate medical notes from audio recordings of doctor-patient consultations. However, there are very few studies on how such systems could be used in clinical practice, how clinicians would adjust to using them, or how system design should be influenced by such considerations. In this paper, we present three rounds of user studies, carried out in the context of developing a medical note generation system. We present, analyse and discuss the participating clinicians' impressions and views of how the system ought to be adapted to be of value to them. Next, we describe a three-week test run of the system in a live telehealth clinical practice. Major findings include (i) the emergence of five different note-taking behaviours; (ii) the importance of the system generating notes in real time during the consultation; and (iii) the identification of a number of clinical use cases that could prove challenging for automatic note generation systems.