ArticleBMC medical informatics and decision making2025
Assessing GPT and DeepL for terminology translation in the medical domain: A comparative study on the human phenotype ontology.
Article in BMC medical informatics and decision making, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed.
- AI-enabled language technologies for language-mediated learning and clinical communication in international undergraduate dental education: a scoping review.Frontiers in medicine · 2026Review
- A systematic multimodal assessment of AI machine translation tools for enhancing access to critical care education internationally.BMC medical education · 2025Article
- Delving Into PubMed Records: How AI-Influenced Vocabulary has Transformed Medical Writing since ChatGPT.Perspectives on medical education · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThis paper presents a comparative study of two state-of-the-art language models, OpenAI's GPT and DeepL, in the context of terminology translation within the medical domain.
methodsThis study was conducted on the human phenotype ontology (HPO), which is used in medical research and diagnosis. Medical experts assess the performance of both models on a set of 120 translated HPO terms and their 180 synonyms, employing a 4-point Likert scale (strongly agree = 1, agree = 2, disagree = 3, strongly disagree = 4). An independent reference translation from the HeTOP database was used to validate the quality of the translation.
resultsThe average Likert rating for the selected HPO terms was 1.29 for GPT-3.5 and 1.37 for DeepL. The quality of the translations was also found to be satisfactory for multi-word terms with greater ontological depth. The comparison with HeTOP revealed a high degree of similarity between the models' translations and the reference translations.
conclusionsStatistical analysis revealed no significant differences in the mean ratings between the two models, indicating their comparable performance in terms of translation quality. The study not only illustrates the potential of machine translation but also shows incomplete coverage of translated medical terminology. This underscores the relevance of this study for cross-lingual medical research. However, the evaluation methods need to be further refined, specific translation issues need to be addressed, and the sample size needs to be increased to allow for more generalizable conclusions.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.