Evidence map›Paper›PMID 40535400›Full record

ArticleCureus2025

Beyond Traditional Simulation: An Exploratory Study on the Effectiveness and Acceptability of ChatGPT‑4o Advanced Voice Mode for Communication Skills Practice Among Medical Students.

Alifya Mukadam, Shruti Suresh, Chris Jacobs

Abstract read
In one paragraph

Article in Cureus, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing 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

8 citing papers in PubMed.

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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

3 authors.

Alifya MukadamMedical Education, Great Western Hospitals NHS Foundation Trust, Swindon, GBR.
Shruti SureshMedical Education, Great Western Hospitals NHS Foundation Trust, Swindon, GBR.
Chris JacobsPsychology, University of Bath, Bath, GBR.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background Effective communication skills are essential for quality medical practice and patient care, yet providing sufficient practice opportunities remains challenging in medical education. Traditional approaches using standardized patients (SPs) face limitations, including high costs and logistical constraints. This study investigates the potential of conversational artificial intelligence (AI) to supplement communication skills practice among medical students. Methodology We conducted a mixed-methods study employing a convergent parallel design with 27 medical students from three UK universities between November 2024 and March 2025. Participants engaged in communication skills roleplay sessions using freely available ChatGPT as an SP. Pre- and post-session assessments measured perceived usefulness and self-reported confidence in three communication domains: dealing with challenging patients, breaking bad news, and counselling anxious patients. The instrument was adapted from the Immersive Technology Evaluation Measure (ITEM). Quantitative data were analyzed using non-parametric tests and ordinal logistic regression, while qualitative responses underwent reflexive thematic analysis. Results Self-reported confidence increased significantly across all communication domains following the ChatGPT simulation. Confidence in dealing with challenging patients increased from a median of 3 to 4 (

Indexed as

artificial intelligencechatgptclinical communication trainingcommunication skillslarge language modelsmedical educationmixed-methods researchsimulated patients

Identifiers

PMID40535400
PMCPMC12175028

What OpenQuestion holds

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Registered trials

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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.