Limitations of Medical Machine Translation: Scoping Review with ☸️SAIMSARA.

SAIMSARA

Editorial note

• Last update: 2026-03-29 21:55:05

What is this paper about

Medical machine translation may look fluent, but this review shows where it still breaks: semantic precision, cultural nuance, audience adaptation, and high-stakes clinical reliability. The full read is worth it because it separates the real strengths of MT in constrained tasks from the specific failure modes that still make expert human oversight essential.

Abstract:

Keywords:

Review Stats

Final search date and database lock: 2026-03-29 21:56:17 CEST

Plan: Pro (expanded craft tokens; source: Semantic Scholar)

Source: Semantic Scholar

Total Abstracts/Papers: 222

Downloaded Abstracts/Papers: 222

Included original and non-original Abstracts/Papers (all): 38

Included original Abstracts/Papers (Vote counting by direction of effect): 17

Reference Index (links used in paper): 15

Total participants (topic deduplicated ΣN): 25663

Get access to the full paper

temporary link or permanent access.

The remaining part of the paper opens after purchase or unlock.

Reference Index (15)

[1] Multilingual LLM Prompting Strategies for Medical English-Vietnamese Machine Translation — https://doi.org/10.48550/arxiv.2509.15640
[2] Evaluating Medical Translation Quality Using Neural Machine Translation Models: A CUI-Based Approach via Concept Embeddings from UMLS Data — https://doi.org/10.1109/icnlp65360.2025.11108351
[3] Translation of Veterinary Texts: Post-Editing for Different Target Audiences Amid MT Limitations — https://doi.org/10.17507/jltr.1602.02
[4] ACES: Translation Accuracy Challenge Sets for Evaluating Machine Translation Metrics — https://doi.org/10.48550/arxiv.2210.15615
[5] A Study on the Syntactic Features of C-E Translation by Large Language Models Based on Dependency Distance: Testing the Compromise Hypothesis and Exploring Quality Assessment — https://doi.org/10.1145/3765325.3765337
[6] Natural Language Processing in Electronic Health Records in relation to healthcare decision-making: A systematic review — https://doi.org/10.1016/j.compbiomed.2023.106649
[7] Supporting Risk-Aware Use of Online Translation Tools in Delivering Mental Healthcare Services among Spanish-Speaking Populations — https://doi.org/10.1155/2021/1011197
[8] A Comparative Study of Human and AI-Generated Spanish Translations of the Huangdi Neijing: Implications for Cross-Cultural Transmission in the Age of Artificial Intelligence — https://doi.org/10.22158/elsr.v6n4p80
[9] Human vs. Machine: Assessing Translation Quality of Four-character Terms in the Classical Chinese Medical Text Huangdi Neijing — https://doi.org/10.9734/air/2025/v26i41405
[10] Understanding the societal impacts of machine translation: a critical review of the literature on medical and legal use cases — https://doi.org/10.1080/1369118x.2020.1776370
[11] The Challenges of Using Machine Translation While Translating Polysemous Words — https://doi.org/10.11114/smc.v11i2.5944
[12] Cohesive Features in Automatic Translation of Medical Texts = التناسق في النص الطبي المترجم آليا = Les Aspects de la Cohésion dans la Traduction Automatique des Textes Médicaux — https://doi.org/10.12816/0055710
[13] Beyond the Algorithm: Investigating Cognitive and Pedagogical Implications of Machine Translation in Algerian Medical Contexts — https://doi.org/10.52919/ijmlsp.v3i01.90
[14] GENERATIVE AI AND OPEN-SOURCE DISTRIBUTION TO PUT EVALUATION TOOLS IN THE HANDS OF CLINICIANS AND PATIENTS: OSTOMY OUTPUT CONSISTENCY SCALE — https://doi.org/10.1093/ibd/izag006.083
[32] Automatic RadLex coding of Chinese structured radiology reports based on text similarity ensemble — https://doi.org/10.1186/s12911-021-01604-9

Limitations of Medical Machine Translation: Scoping Review with ☸️SAIMSARA.

DOI: 10.62487/saimsarae1a843d5

Get access to the full paper