Evidence map›Paper›PMID 42104646›Full record

ArticleCroatian medical journal2026

Can artificial intelligence create human touch in medical writing? A pilot study.

Shigeki Matsubara

Abstract read
In one paragraph

Article in Croatian medical journal, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the 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.

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

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

1 author.

Shigeki MatsubaraShigeki Matsubara, Department of Obstetrics and Gynecology, Jichi Medical University, 3311-1, Yakushiji, Shimotsuke, Tochigi 329-0498, Japan, matsushi@jichi.ac.jp.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

aimTo assess whether ChatGPT can autonomously generate and select "human touch" elements (anecdotes, beliefs, and old sayings) and produce writing comparable to human-authored manuscripts.

methodsA disagreement letter was composed and then tasked ChatGPT-5 with writing a new disagreement letter. The model was instructed to select suitable anecdotes from a candidate list and generate new ones. Both letters were compared. Eight experienced researchers independently assessed whether the letters were appealing.

resultsChatGPT was able to select appropriate elements from the candidate list and, importantly, generate new ones. The human-generated letter was found to be more appealing by five of eight reviewers, and the ChatGPT-generated letter by three reviewers. None of the researchers reported that they found the use of human touch inappropriate or disruptive. Conclusion Although a single case was studied, these findings may help inform reflection on the use of LLMs in medical writing.

Indexed as

Medical WritingTouchWritingGenerative Artificial IntelligenceHumansLarge Language ModelsPilot Projects

Identifiers

PMID42104646
PMCPMC13176933

What OpenQuestion holds

Textmetadata
LicenceCC BY
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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.