Evidence map›Paper›PMID 42902927›Full record

Trial reportJMIR medical education2026

Effects of a Generative AI Virtual Patient on Serious Illness Communication Skills: Randomized Controlled Trial.

Kurtis G Haut, Masum Hasan, Thomas Carroll, Ronald M Epstein, Taylan Sen, Ehsan Hoque

Abstract readRandomized Controlled Trial
In one paragraph

Trial report in JMIR medical education, 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

6 authors.

Kurtis G Haut *Department of Computer Science, University of Rochester, 3013 Wegmans Hall, Rochester, NY, 14627, United States, +1 (585) 275-1351.ORCID http://orcid.org/0000-0001-8344-1618
Masum Hasan *Department of Computer Science, University of Rochester, 3013 Wegmans Hall, Rochester, NY, 14627, United States, +1 (585) 275-1351.ORCID http://orcid.org/0000-0001-8682-522X
Thomas CarrollDepartment of Medicine, Divisions of General Medicine and Palliative Care, University of Rochester Medical Center, Rochester, NY, United States.ORCID http://orcid.org/0000-0003-4887-9651
Ronald M EpsteinDepartments of Family Medicine and Medicine, University of Rochester Medical Center, Rochester, NY, United States.ORCID http://orcid.org/0000-0002-3564-9163
Taylan SenComputer and Information Sciences, Niagara University, Niagara Falls, NY, United States.ORCID http://orcid.org/0000-0002-4962-2922
Ehsan HoqueDepartment of Computer Science, University of Rochester, 3013 Wegmans Hall, Rochester, NY, 14627, United States, +1 (585) 275-1351.ORCID http://orcid.org/0000-0003-4781-4733

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Serious illness communication (SIC) is a critical skill in medical education, especially in end-of-life care, yet clinicians often lack sufficient opportunities for deliberate practice. Standardized patients (SPs) remain the gold standard for SIC training, but they are limited by cost, availability, and consistency. To support wider access to SIC training, scalable alternatives are needed. Objective: The aim of this study is to evaluate whether SOPHIE (Standardized Online Patient for Healthcare Interaction Education), an AI-powered virtual patient that provides real-time dialogue and automated feedback, improves SIC skills among clinicians and trainees. Methods: We conducted a single-blind, parallel-group, randomized controlled trial from June to December 2024 via videoconferencing. Participants were randomized in a 1:1 ratio to the intervention (SOPHIE training) or control (reading module) group. Blinded SP actors and 4 independent raters evaluated outcomes preintervention and postintervention. The intervention group completed 3 interactive SOPHIE modules (Empathize, Be Explicit, and Empower) for 30 minutes, combining dialogue with an interactive virtual patient and automated feedback. Control participants reviewed reading materials covering the same framework for the same duration. The primary outcome was the change in communication skills (Empathize, Be Explicit, and Empower) assessed by SPs and blinded raters using a rubric based on an established clinical communication training framework. Secondary outcomes included self-reported confidence in communication skills. Results: Fifty-one medical, nursing, and physician assistant students and practicing clinicians (mean age 30.6, SD 13.2 years; women: 40/51, 78.4%) were recruited from academic and clinical settings. Participants were stratified by training level and randomized; all completed the study. Compared with controls, SOPHIE participants demonstrated significantly greater improvement in Empower (β=.11, 95% CI .04-.17; Conclusions: Training with an AI-powered virtual patient significantly improved SIC skills and confidence compared with reading modules. Generative AI-based simulations may provide a scalable, accessible, and effective adjunct or alternative to traditional SP training in clinical education.

Indexed as

CommunicationPatient SimulationAdultClinical CompetenceFemaleGenerative Artificial IntelligenceHumansMaleMiddle AgedPhysician-Patient RelationsSingle-Blind MethodAIend-of-life caregenerative AIlarge language modelsmedical educationpalliative carerandomized controlled trialserious illness communicationsimulation-based trainingvirtual standardized patients

Identifiers

PMID42902927
PMCPMC13683997

What OpenQuestion holds

Textmetadata
Read underepoch 390

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.