Evidence map›Paper›PMID 41322766›Full record

ArticleCureus2025

A Pilot Test of an AI Voice-Driven Simulation With Feedback for Medical Students to Practice Discussing Diagnostic Mammogram Results With Patients.

Warren S Comulada, Patricia A Ganz, Yue Ming Huang, Dan Weisman, Alanna Sugarman, Daniel Lee, Saachi Shah, Lillian Gelberg

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 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

8 authors.

Warren S ComuladaHealth Policy and Management, University of California Los Angeles (UCLA) Fielding School of Public Health, Los Angeles, USA.
Patricia A GanzOncology, University of California Los Angeles (UCLA) David Geffen School of Medicine, Los Angeles, USA.
Yue Ming HuangMedical Education and Simulation, University of California Los Angeles (UCLA) Simulation Center, Los Angeles, USA.
Dan WeismanMedical Education and Simulation, University of California Los Angeles (UCLA) Simulation Center, Los Angeles, USA.
Alanna SugarmanMedicine, University of California Los Angeles (UCLA) David Geffen School of Medicine, Los Angeles, USA.
Daniel LeeFamily Medicine, University of California Los Angeles (UCLA) David Geffen School of Medicine, Los Angeles, USA.
Saachi ShahPsychology, University of California Los Angeles (UCLA), Los Angeles, USA.
Lillian GelbergFamily Medicine, University of California Los Angeles (UCLA) David Geffen School of Medicine, Los Angeles, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction Effective communication when delivering sensitive results or bad news to patients is necessary for medical professionals, warranting greater attention in medical education. The opportunity to practice communication skills with human standardized patients has limitations, including not being readily available, prompting researchers to develop virtual patients (VPs) to supplement training with a focus on routine tasks. This study capitalized on artificial intelligence (AI) advances to develop a VP for more complex dialogs where learners practice discussing uncertain screening results with patients. Methods We conducted a single-arm pilot study with 10 medical students to evaluate the feasibility and acceptability of a simulated telephone call between a physician (learner) and a VP to discuss the patient's abnormal mammogram results that require breast biopsy examination. Objectives culminate in the VP agreeing to schedule a biopsy. Afterward, an AI agent displays feedback for the learner. Results Study completion demonstrated feasibility, with quantitative measures indicating acceptability. The average System Usability Scale score of 91 was high (range, 78-100). For Technology Acceptance Model questions, most students "agreed" or "strongly agreed" that VP responses "seemed reasonable", AI agent-generated feedback was "helpful", and VP training would increase their "confidence communicating with patients". Qualitative feedback solidified these findings, highlighting the simulation's realism, detailed feedback, and perceived effectiveness in improving communication skills. Conclusion The study recommends further evaluation for including VPs in medical education, especially to help learners practice difficult conversations with patients. Future developments will expand the scenario and evaluate communication skills outcomes while continuing to refine the VP and feedback.

Indexed as

breast biopsygpt-4large language model (llm)mammographyscreen-based simulationteaching communication skillsvirtual standardized patient

Identifiers

PMID41322766
PMCPMC12659812

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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.