ArticleFrontiers in digital health2026
The MediVoice implementation journey: ambient artificial intelligence for clinical documentation.
Article in Frontiers in digital health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- Beyond Transparency : Ethical Note Obligations, Documentation Burden, and the Ethical Adoption of AI-Assisted Documentation.Journal of bioethical inquiry · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Healthcare systems are increasingly turning to ambient Artificial Intelligence (AI) scribes to reduce documentation burden and lighten clinicians' cognitive load. In this brief research report, we introduce MediVoice, an ambient AI scribe developed and implemented within the National University Health System, Singapore. MediVoice was piloted across multiple clinical settings and rapidly evaluated through Plan-Do-Study-Act cycles. Doctors, nurses, and allied health professionals assessed its usability, accuracy, workflow fit, and potential time savings. Real-time feedback informed iterative refinement, enabling organisational learning and reinforcing the value of experimentation during early AI adoption. Broader deployment required leadership engagement, an AI community of practice, role-specific training, user champions, recognition of its value, and supporting digital and organisational infrastructure. Looking ahead, routine use will require integration with the electronic medical record, enhanced speech recognition capabilities, and robust AI governance frameworks. MediVoice's trajectory shows that while ambient AI scribes can offer meaningful benefits, success requires more than technical capability. Effective implementation needs continuous adaptation, workflow alignment, cross-professional engagement, governance, and organisational readiness. This case study offers practical lessons for health systems seeking to introduce ambient AI tools within clinical environments.
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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.