Evidence map›Paper›PMID 42542459›Full record

ArticleNPJ digital medicine2026

Reply to: Clinician engagement modifies effectiveness of AI enabled ECG screening.

Alberto Calleri, Yigit Yazarkan, Douglas A Simonetto

Abstract readLetter
In one paragraph

Article in NPJ digital medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Alberto CalleriDivision of Gastroenterology and Hepatology, Mayo Clinic Rochester, Rochester, MN, USA.
Yigit YazarkanDivision of Gastroenterology and Hepatology, Mayo Clinic Rochester, Rochester, MN, USA.
Douglas A SimonettoDivision of Gastroenterology and Hepatology, Mayo Clinic Rochester, Rochester, MN, USA. Simonetto.Douglas@mayo.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Chen highlights the importance of distinguishing algorithmic performance from deployment stability in AI-enabled screening. Drawing on a post hoc analysis evaluating AI-enabled ECG screening for chronic liver disease in primary care, we discuss how clinician engagement, workflow integration, and alert burden may influence the translation of AI algorithms into clinical practice. These observations reinforce the view that implementation is a key determinant of the real-world effectiveness of AI-enabled care.

Identifiers

PMID42542459
PMCPMC13428743

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.