AI as a resource for the clarification of medical terminology
Bibliographic record
Abstract
Abstract Medical terminology is perceived as an obstacle for patients and family members to understand the medical message. In this context, plain language advocates for making specialised knowledge accessible to citizens. This article puts the synergy between medical terminology, plain language, and computational linguistics (a branch of Artificial Intelligence) on the table. Our purpose is to determine if Generative AI applications, like ChatGPT, can assist in creating glossaries of terms with their corresponding simple variants. For this, a relevant sub-field of medicine, cardiology, was taken as a case study, even though it could be extrapolated to other sub-fields. Next, a glossary of key cardiology terms was created following a classic methodological approach. Then, the most relevant phases of the process (terminology extraction, search for synonyms, and selection of the clearest synonym) were reproduced using ChatGPT. The results of the comparative study between both approaches give a glimpse of the extent to which this technology can be a useful resource for creating glossaries that can be employed for writing using plain language.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.011 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".