# Automatic Glossary of Clinical Terminology: a Large-Scale Dictionary of Biomedical Definitions Generated from Ontological Knowledge

arXiv: https://arxiv.org/abs/2306.00665 · HTML: https://mlsanity.com/paper/2306.00665/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 17 references that are in the map.
- First posted on arXiv 1 Jun 2023.

## Builds on (most cited first, 4 of 4)

- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [Investigating Pretrained Language Models for Graph-to-Text Generation](https://mlsanity.com/paper/2007.08426/) (2020)
- [BioLORD: Learning Ontological Representations from Definitions (for Biomedical Concepts and their Textual Descriptions)](https://mlsanity.com/paper/2210.11892/) (2022)
- [Characterizing Attribution and Fluency Tradeoffs for Retrieval-Augmented Large Language Models](https://mlsanity.com/paper/2302.05578/) (2023)

## Built on by (most cited first, 5 of 5)

- [A Survey of Scientific Large Language Models: From Data Foundations to Agent Frontiers](https://mlsanity.com/paper/2508.21148/) (2025)
- [README: Bridging Medical Jargon and Lay Understanding for Patient Education through Data-Centric NLP](https://mlsanity.com/paper/2312.15561/) (2023)
- [BioLORD-2023: Semantic Textual Representations Fusing LLM and Clinical Knowledge Graph Insights](https://mlsanity.com/paper/2311.16075/) (2023)
- [A Natural Language Processing Approach to Support Biomedical Data Harmonization: Leveraging Large Language Models](https://mlsanity.com/paper/2411.02730/) (2024)
- [Language corpora for the Dutch medical domain](https://mlsanity.com/paper/2604.25374/) (2026)

## Abstract (arXiv)

Background: More than 400,000 biomedical concepts and some of their relationships are contained in SnomedCT, a comprehensive biomedical ontology. However, their concept names are not always readily interpretable by non-experts, or patients looking at their own electronic health records (EHR). Clear definitions or descriptions in understandable language are often not available. Therefore, generating human-readable definitions for biomedical concepts might help make the information they encode more accessible and understandable to a wider public. Objective: In this article, we introduce the Automatic Glossary of Clinical Terminology (AGCT), a large-scale biomedical dictionary of clinical concepts generated using high-quality information extracted from the biomedical knowledge contained in SnomedCT. Methods: We generate a novel definition for every SnomedCT concept, after prompting the OpenAI Turbo model, a variant of GPT 3.5, using a high-quality verbalization of the SnomedCT relationships of the to-be-defined concept. A significant subset of the generated definitions was subsequently judged by NLP researchers with biomedical expertise on 5-point scales along the following three axes: factuality, insight, and fluency. Results: AGCT contains 422,070 computer-generated definitions for SnomedCT concepts, covering various domains such as diseases, procedures, drugs, and anatomy. The average length of the definitions is 49 words. The definitions were assigned average scores of over 4.5 out of 5 on all three axes, indicating a majority of factual, insightful, and fluent definitions. Conclusion: AGCT is a novel and valuable resource for biomedical tasks that require human-readable definitions for SnomedCT concepts. It can also serve as a base for developing robust biomedical retrieval models or other applications that leverage natural language understanding of biomedical knowledge.