Evidence map›Paper›PMID 39776453›Full record

ArticleFocus (American Psychiatric Publishing)2025

Artificial Intelligence Scribes in Psychiatry.

Patrick Buckley, Yanshan Wang, Priya Gopalan

Abstract read
In one paragraph

Article in Focus (American Psychiatric Publishing), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

  1. Review
  2. Review
  3. Article
  4. Article
  5. Review
  6. 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.

Patrick BuckleyDepartment of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania (Buckley, Gopalan); Department of Health Information Management, University of Pittsburgh, Pittsburgh, Pennsylvania (Wang).
Yanshan WangDepartment of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania (Buckley, Gopalan); Department of Health Information Management, University of Pittsburgh, Pittsburgh, Pennsylvania (Wang).
Priya GopalanDepartment of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania (Buckley, Gopalan); Department of Health Information Management, University of Pittsburgh, Pittsburgh, Pennsylvania (Wang).

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Artificial intelligence (AI) scribes for clinical documentation are likely to be among the first AI tools to affect the day-to-day practice of psychiatry, yet many psychiatrists are unfamiliar with them. This article introduces psychiatrists to AI scribes, including their potential benefits and risks. AI scribes may enhance efficiency, reduce physician burnout, and improve patient-physician rapport by automating documentation processes. Key concerns include privacy, accuracy, and the ability to capture nuanced patient communication. Initial recommendations for responsibly integrating AI scribes into psychiatric practice are provided, emphasizing the importance of balancing technological benefits with careful oversight.

Indexed as

AI scribeArtificial intelligenceburnout, physician issuescomputers

Identifiers

PMID39776453
PMCPMC11701814

What OpenQuestion holds

Textmetadata
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.