ReviewDiscover public health2025
Legal, ethical, and policy challenges of artificial intelligence translation tools in healthcare.
Review in Discover public health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 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
4 citing papers in PubMed.
- Google Translate Voice App vs. Qualified Interpreters: An Exploratory Study of Clinical Accuracy in Real-World Speech/Voice Encounters.Healthcare (Basel, Switzerland) · 2026Article
- Article
- Comparative Evaluation of ChatGPT, Google Translate, and UD Talk for Chinese-to-Japanese Translation in Cardiology and Pulmonology Outpatient Consultations: Prospective Observational Study.Journal of medical Internet research · 2026Observational
- Guiding Policymakers Toward Better AI Ethics Integration in Healthcare Regulation-Lessons from Singapore.Journal of clinical medicine · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial intelligence (AI) translation tools, such as Google Translate and ChatGPT, are increasingly used in healthcare for medical communication to overcome language barriers between patients and providers. While these tools offer accessible and efficient translation, their use raises significant legal, ethical, and policy concerns. Key patients' rights, including the rights to privacy, informed consent, and equitable access to care, may be compromised. Current European regulations, including the EU AI Act, General Data Protection Regulation, and Medical Devices Regulation, offer only partial protection, leaving important regulatory gaps. This study employs a mixed-methods approach combining legal doctrinal analysis of EU regulatory frameworks with manual content analysis of platform terms of service. It integrates interdisciplinary perspectives from bioethics, digital health, and health communication to evaluate the implications of AI-mediated translation in clinical care. Findings reveal persistent and overlapping risks: violations of data privacy, inaccuracies in translation, bias and discrimination, and unclear liability when errors occur. To mitigate these risks, we propose targeted policy interventions, including developing guidelines for AI translation use in healthcare settings. This article contributes to digital health policy debates by identifying legal pathways to regulate AI translation tools in healthcare, ensuring their use supports patients' rights and promotes health equity.
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.