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This paper is in the following e-collection/theme issue:

Pilot studies (ehealth) (355) Quality/Credibility of eHealth Information and Trust Issues (679) Natural Language Processing (965) Language Translation Technologies for Medicine and Public Health (34) Generative Language Models Including ChatGPT (832) AI Language Models in Health Care (259)

Published on in Vol 10 (2026)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/85169, first published .
Comparing Large Language Models and Traditional Machine Translation Tools for Translating Medical Consultation Summaries: Quantitative Pilot Feasibility Study

Comparing Large Language Models and Traditional Machine Translation Tools for Translating Medical Consultation Summaries: Quantitative Pilot Feasibility Study

Comparing Large Language Models and Traditional Machine Translation Tools for Translating Medical Consultation Summaries: Quantitative Pilot Feasibility Study

Authors of this article:

Andy Li1 Author Orcid Image ;   Wei Zhou1 Author Orcid Image ;   Rashina Hoda1 Author Orcid Image ;   Chris Bain1 Author Orcid Image ;   Peter Poon2, 3 Author Orcid Image
Article Authors Cited by Tweetations Metrics

Andy Li   1 , BEE, BCS ;   Wei Zhou   1 , PhD ;   Rashina Hoda   1 , PhD ;   Chris Bain   1 , PhD ;   Peter Poon   2, 3 , MBBS, PGDipPM, CHIA

1 Faculty of Information Technology, Monash University, Clayton, Australia

2 Faculty of Medicine, Nursing and Health Sciences, Monash University, Monash, Australia

3 Supportive and Palliative Care Unit, Monash Health, Monash, Australia

Corresponding Author:

  • Rashina Hoda, PhD
  • Faculty of Information Technology, Monash University
  • Room 221, Clayton Campus, 20 Exhibition Walk
  • Clayton 3800
  • Australia
  • Phone: +61 3 9902 9970
  • Email: Rashina.hoda@monash.edu