Using artificial intelligence based language interpretation in non-urgent paediatric emergency consultations: a clinical performance test and legal evaluation
Abstract Objective To evaluate the accuracy of Google Translate (GT) in translating low-acuity paediatric emergency consultations involving respiratory symptoms and fever, and to examine legal and policy implications of using AI-based language interpretation in healthcare. Methods Based on the metho...
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| Language: | English |
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BMC
2025-01-01
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| Series: | BMC Health Services Research |
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| Online Access: | https://doi.org/10.1186/s12913-025-12263-1 |
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| author | Julia Brandenberger Ian Stedman Noah Stancati Karen Sappleton Sarathy Kanathasan Jabeen Fayyaz Devin Singh |
| author_facet | Julia Brandenberger Ian Stedman Noah Stancati Karen Sappleton Sarathy Kanathasan Jabeen Fayyaz Devin Singh |
| author_sort | Julia Brandenberger |
| collection | DOAJ |
| description | Abstract Objective To evaluate the accuracy of Google Translate (GT) in translating low-acuity paediatric emergency consultations involving respiratory symptoms and fever, and to examine legal and policy implications of using AI-based language interpretation in healthcare. Methods Based on the methodology used for conducting language performance testing routinely at the Interpreter Services Department of the Hospital for Sick Children, clinical performance testing was completed using a paediatric emergency scenario (child with respiratory illness and fever) on five languages: Spanish, French, Urdu, Arabic, and Mandarin. The study focused on GT's translation accuracy and a legal and policy evaluation regarding AI-based interpretation in healthcare was conducted by legal scholars. Results GT demonstrated strong translation performance, with accuracy rates from 83.5% in Urdu to 95.4% in French. Challenges included dialect sensitivity and pronoun misinterpretations. Legal evaluation indicated inconsistent access to language interpretation services across healthcare jurisdictions and potential risks involving data privacy, consent, and malpractice when using AI-based translation tools. Conclusions Google Translate can effectively support communication in specific non-critical paediatric emergency scenarios. However, its use necessitates careful monitoring, understanding of its limitations, and attention to dialect and literal translation risks along with equity considerations. Establishing legal and policy frameworks for language interpretation in healthcare is crucial, alongside addressing funding and data security concerns, to optimize the use of AI-based translation tools in healthcare contexts. |
| format | Article |
| id | doaj-art-2e5dee4e1ee745eca7b3ff83e9ac68c1 |
| institution | Kabale University |
| issn | 1472-6963 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Health Services Research |
| spelling | doaj-art-2e5dee4e1ee745eca7b3ff83e9ac68c12025-01-26T12:22:06ZengBMCBMC Health Services Research1472-69632025-01-0125111010.1186/s12913-025-12263-1Using artificial intelligence based language interpretation in non-urgent paediatric emergency consultations: a clinical performance test and legal evaluationJulia Brandenberger0Ian Stedman1Noah Stancati2Karen Sappleton3Sarathy Kanathasan4Jabeen Fayyaz5Devin Singh6Division of Paediatric Emergency Medicine, Department of Paediatrics, Inselspital, Bern University Hospital, University of BernSchool of Public Policy and Administration, York UniversityDivision of Pediatric Emergency Medicine, Hospital for Sick ChildrenLanguage Interpretation Services, Hospital for Sick ChildrenDivision of Pediatric Emergency Medicine, Hospital for Sick ChildrenDivision of Pediatric Emergency Medicine, Hospital for Sick ChildrenDivision of Pediatric Emergency Medicine, Hospital for Sick ChildrenAbstract Objective To evaluate the accuracy of Google Translate (GT) in translating low-acuity paediatric emergency consultations involving respiratory symptoms and fever, and to examine legal and policy implications of using AI-based language interpretation in healthcare. Methods Based on the methodology used for conducting language performance testing routinely at the Interpreter Services Department of the Hospital for Sick Children, clinical performance testing was completed using a paediatric emergency scenario (child with respiratory illness and fever) on five languages: Spanish, French, Urdu, Arabic, and Mandarin. The study focused on GT's translation accuracy and a legal and policy evaluation regarding AI-based interpretation in healthcare was conducted by legal scholars. Results GT demonstrated strong translation performance, with accuracy rates from 83.5% in Urdu to 95.4% in French. Challenges included dialect sensitivity and pronoun misinterpretations. Legal evaluation indicated inconsistent access to language interpretation services across healthcare jurisdictions and potential risks involving data privacy, consent, and malpractice when using AI-based translation tools. Conclusions Google Translate can effectively support communication in specific non-critical paediatric emergency scenarios. However, its use necessitates careful monitoring, understanding of its limitations, and attention to dialect and literal translation risks along with equity considerations. Establishing legal and policy frameworks for language interpretation in healthcare is crucial, alongside addressing funding and data security concerns, to optimize the use of AI-based translation tools in healthcare contexts.https://doi.org/10.1186/s12913-025-12263-1Pediatric emergency medicineArtificial intelligenceMinor health visitsNon-urgent health visitsPediatric migrant healthPediatrics |
| spellingShingle | Julia Brandenberger Ian Stedman Noah Stancati Karen Sappleton Sarathy Kanathasan Jabeen Fayyaz Devin Singh Using artificial intelligence based language interpretation in non-urgent paediatric emergency consultations: a clinical performance test and legal evaluation BMC Health Services Research Pediatric emergency medicine Artificial intelligence Minor health visits Non-urgent health visits Pediatric migrant health Pediatrics |
| title | Using artificial intelligence based language interpretation in non-urgent paediatric emergency consultations: a clinical performance test and legal evaluation |
| title_full | Using artificial intelligence based language interpretation in non-urgent paediatric emergency consultations: a clinical performance test and legal evaluation |
| title_fullStr | Using artificial intelligence based language interpretation in non-urgent paediatric emergency consultations: a clinical performance test and legal evaluation |
| title_full_unstemmed | Using artificial intelligence based language interpretation in non-urgent paediatric emergency consultations: a clinical performance test and legal evaluation |
| title_short | Using artificial intelligence based language interpretation in non-urgent paediatric emergency consultations: a clinical performance test and legal evaluation |
| title_sort | using artificial intelligence based language interpretation in non urgent paediatric emergency consultations a clinical performance test and legal evaluation |
| topic | Pediatric emergency medicine Artificial intelligence Minor health visits Non-urgent health visits Pediatric migrant health Pediatrics |
| url | https://doi.org/10.1186/s12913-025-12263-1 |
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