ReviewBritish dental journal2026
For the record: a narrative review of ambient voice technology in clinical documentation for dentistry and wider healthcare.
Review in British dental journal, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
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
Aims We synthesise current evidence on ambient voice technologies (AVT) and generative artificial intelligence (AI) in dentistry and wider healthcare.Methods Databases were searched across AI and AI-enabled clinical documentation, large language models and summarisation, and documentation outputs. Included studies evaluated AI-generation of clinical documentation with outcome measures including accuracy, documentation time, and the clinician/patient experience.Results Evidence within dentistry is limited, with only one current study evaluating AVT speech-to-text accuracy. Performance varied between systems, with reduced accuracy for dental terminology and clinically significant errors and hallucinations observed. No dental studies evaluated AI-generation of clinical records or letters. Studies across wider healthcare reported variable performance, including errors with the potential to cause harm. Implementation studies suggest reductions in documentation time and burden with AVT; however, the magnitude of effect was inconsistent, with uptake varying between clinicians and note-length increased. Patient attitudes were favourable although conditional on the accuracy and privacy of using these tools.Conclusions Ambient AI may reduce documentation time, but clinicians remain responsible for the final record produced by these systems. Ongoing research into safety and efficiency, alongside improved AI-literacy is required to ensure that the benefits of this technology are realised without compromising record accuracy or patient trust.
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.