Evidence map›Paper›PMID 42733550›Full record

ArticleMayo Clinic proceedings. Digital health2026

Randomized Controlled Trial Comparing AI-Generated, Clinician-Edited to Human-Generated After-Visit Summaries.

Jonah Zaretsky, Christopher Kim, Vincent Major, Benjamin Verplanke, Christopher Sonne, William Robert Small, Priyanka Solanki, Lucille Fenelon, Nilufar Tursunova, Alyssa Gutjahr and 5 more

Registry-linked trialAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Mayo Clinic proceedings. Digital health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06711458 (Using Generative Artificial Intelligence to Transform Discharge Summaries Into Patient-Friendly Language and Format), which is not on this map. Not yet cited in PubMed.

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

NCT06711458 nacompletednot on this map

Using Generative Artificial Intelligence to Transform Discharge Summaries Into Patient-Friendly Language and Format

TypeinterventionalSponsorNYU Langone HealthRan2025 to 2025Enrolled61ConditionsHospital Discharge Summary UnderstandingArmsAI-Generated Discharge Instructions, Standard of Care Discharge Instructions
3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

15 authors.

Jonah ZaretskyNYU Langone Health, Department of Medicine, New York, NY.
Christopher KimNYU Langone Health, Department of Medicine, New York, NY.
Vincent MajorNYU Langone Health, Department of Population Health, New York, NY.
Benjamin VerplankeNYU Langone Health, Department of Medicine, New York, NY.
Christopher SonneNYU Grossman School of Medicine, Department of Medicine, New York, NY.
William Robert SmallNYU Grossman School of Medicine, Department of Medicine, New York, NY.
Priyanka SolankiNYU Langone Health, MCIT Department of Health Informatics New York, NY.
Lucille FenelonNYU Langone Health, MCIT, New York, NY.
Nilufar TursunovaNYU Langone Health, MCIT Department of Health Informatics New York, NY.
Alyssa GutjahrNYU Langone Health, MCIT, New York, NY.
Yunan ZhaoNYU Langone Health, Department of Population Health, New York, NY.
Saul BleckerNYU Grossman School of Medicine, Department of Population Health, New York, NY.
Jonathan AustrianNYU Grossman School of Medicine, Department of Medicine, New York, NY.
Paul TestaNYU Langone Health, MCIT Department of Health Informatics New York, NY.
Jonah FeldmanNYU Langone Health, MCIT Department of Health Informatics New York, NY.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: To test whether AI-generated, clinician-edited after-visit summaries (AVS) are more patient-friendly than clinician-generated AVS, without compromising safety, when implemented in a live inpatient setting. Patients and Methods: Patients discharged from inpatient medicine units between January 22, 2025, and May 23, 2025, were randomized to the intervention arm, which included AI-generated, clinician-edited AVS, or the control arm, which included usual care of clinician-only AVS. Outcomes include text between 6 Results: Among 61 patients enrolled in the trial, the AI-generated, clinician-edited AVS were significantly more likely to have a mean reading level closer to 6 Conclusion: In this small, randomized trial, exploratory and component measures suggest improved patient-friendliness in AI-generated, clinician-edited AVS, although no significant difference was observed in the primary outcome. ClinicalTrialsgov number: NCT06711458.

Identifiers

PMID42733550
PMCPMC13571222

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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.