Automated Medical Text Simplification for Enhanced Patient Access

Liliya Makhmutova, Giancarlo Salton, Fernando Perez-Tellez, Robert Ross

2024

Abstract

Doctors and patients have significantly different mental models related to the medical domain; this can lead to different preferences in terminology used to describe the same concept, and in turn, makes medical text often difficult to understand for the average person. However, getting access to a good understanding of patient notes, medical history, and other health-related documents is crucial for patients’ recovery and sticking to a diet or medical procedures. Large language models (LLM) can be used to simplify and summarize text, yet there is no guarantee that the output will be correct and contain all the needed information. In this paper, we create and propose a new multi-modal medical text simplification dataset with pictorial explanations following along the aligned simplified and use it to evaluate the current state-of-the-art large language model (SOTA LLM) for the simplification task for the dataset and compare it to human-written texts. Our findings suggest that the current general-purpose LLMs are still not reliable enough for such in the medical sphere, though they may simplify texts quite well. The dataset and additional materials may be found at https://github.com/ LiliyaMakhmutova/medical texts simplification.

Paper Citation

in Harvard Style

Makhmutova L., Salton G., Perez-Tellez F. and Ross R. (2024). Automated Medical Text Simplification for Enhanced Patient Access. In Proceedings of the 17th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 2: HEALTHINF; ISBN 978-989-758-688-0, SciTePress, pages 208-218. DOI: 10.5220/0012466100003657

in Bibtex Style

@conference{healthinf24,
author={Liliya Makhmutova and Giancarlo Salton and Fernando Perez-Tellez and Robert Ross},
title={Automated Medical Text Simplification for Enhanced Patient Access},
booktitle={Proceedings of the 17th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 2: HEALTHINF},
year={2024},
pages={208-218},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012466100003657},
isbn={978-989-758-688-0},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 2: HEALTHINF
TI - Automated Medical Text Simplification for Enhanced Patient Access
SN - 978-989-758-688-0
AU - Makhmutova L.
AU - Salton G.
AU - Perez-Tellez F.
AU - Ross R.
PY - 2024
SP - 208
EP - 218
DO - 10.5220/0012466100003657
PB - SciTePress