Evidence map›Paper›PMID 42527471›Full record

Articlenpj health systems2025

A systematic review of early evidence on generative AI for drafting responses to patient messages.

Di Hu, Yawen Guo, Yiliang Zhou, Lidia Flores, Kai Zheng

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in npj health systems, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing 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

11 citing papers in PubMed.

  1. Article
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  5. Review
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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

5 authors.

Di HuDepartment of Informatics, Donald Bren School of Information and Computer Sciences, University of California, Irvine, Irvine, CA, USA. dih11@uci.edu.
Yawen GuoDepartment of Informatics, Donald Bren School of Information and Computer Sciences, University of California, Irvine, Irvine, CA, USA.
Yiliang ZhouDepartment of Informatics, Donald Bren School of Information and Computer Sciences, University of California, Irvine, Irvine, CA, USA.
Lidia FloresDepartment of Informatics, Donald Bren School of Information and Computer Sciences, University of California, Irvine, Irvine, CA, USA.
Kai ZhengDepartment of Informatics, Donald Bren School of Information and Computer Sciences, University of California, Irvine, Irvine, CA, USA. kai.zheng@uci.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This systematic review synthesizes currently available empirical evidence on generative artificial intelligence (GenAI) tools for drafting responses to patient messages. Across a total of 23 studies identified, GenAI was found to produce empathetic replies with quality comparable to that of responses drafted by human experts, demonstrating its potential to facilitate patient-provider communication and alleviate clinician burnout. Challenges include inconsistent performance, risks to patient safety, and ethical concerns around transparency and oversight. Additionally, utilization of the technology remains limited in real-world settings, and existing evaluation efforts vary greatly in study design and methodological rigor. As this field evolves, there is a critical need to establish robust and standardized evaluation frameworks, develop practical guidelines for disclosure and accountability, and meaningfully engage clinicians, patients, and other stakeholders. This review may provide timely insights into informing future research of GenAI and guiding the responsible integration of this technology into day-to-day clinical work.

Identifiers

PMID42527471
PMCPMC13354203

What OpenQuestion holds

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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.