Evidence map›Paper›PMID 41650281›Full record

ArticleJMIR medical informatics2026

AI Scribes: Are We Measuring What Matters?

Enrico Coiera, David Fraile-Navarro

Abstract readEditorial
In one paragraph

Article in JMIR medical informatics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

2 authors.

Enrico CoieraAustralian Institute of Health Innovation, Macquarie University, Level 6, 75 Talavera Road, Sydney, NSW, 2109, Australia, 61 2 9850 2403.ORCID 0000-0002-6444-6584
David Fraile-NavarroAustralian Institute of Health Innovation, Macquarie University, Level 6, 75 Talavera Road, Sydney, NSW, 2109, Australia, 61 2 9850 2403.ORCID 0000-0002-1108-7071

Funding

National Health and Medical Research Council GNT2008645
6 · The paper itself

Abstract

Unlabelled: Artificial intelligence (AI) scribes, software that can convert speech into concise clinical documents, have achieved remarkable clinical adoption at a pace rarely seen for digital technologies in health care. The reasons for this are understandable: the technology works well enough, it addresses a genuine pain point for clinicians, and it has largely sidestepped regulatory requirements. In many ways, clinical adoption of AI scribes has also occurred well ahead of robust evidence of their safety and efficacy. The papers in this theme issue demonstrate real progress in the technology and evidence of its benefit: documentation times are reported to decrease when using scribes, clinicians report feeling less burdened, and the notes produced are often of reasonable quality. Yet as we survey the emerging evidence base, there remains one outstanding and urgent unanswered question: Are AI scribes safe? We need to know the clinical outcomes achievable when scribes are used compared to other forms of note taking.

Indexed as

Artificial IntelligenceDocumentationHumansAIartificial intelligencedigital scribeselectronic documentationerror preventionmedical scribespatient safety

Identifiers

PMID41650281
PMCPMC12880588

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.