Evidence map›Paper›PMID 41269495›Full record

ArticleThe ultrasound journal2025

Effectiveness of traditional, artificial intelligence-assisted, and virtual reality training modalities for focused cardiac ultrasound skill acquisition: a randomised controlled study.

Yie Hui Lau, Sanchalika Acharyya, Cadence Wei Lin Wee, Huiying Xu, Rafael Pulido Saclolo, Kelly Cao, Wee Kim Fong

Registry-linked trialAbstract read
In one paragraph

Article in The ultrasound journal, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06355557 (Human vs Machine), which is not on this map. Cited by 4 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed, 1 pooled it
–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.

NCT06355557 naunknown statusnot on this map

Human vs Machine: a Randomised Controlled Trial Comparing Traditional In-person Instruction, Artificial Intelligence Versus Virtual Reality for Learning Basic Critical Care Echocardiography

TypeinterventionalSponsorTan Tock Seng HospitalRan2024 to 2025Enrolled66ConditionsUltrasoundArmsAI enabled ultrasound system for self-directed learning, Simulator for self-directed learning, traditional with human instructors
3 · Its place in the literature

Who cites it

4 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Review
  4. 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

7 authors.

Yie Hui LauAnaesthesiology, Intensive Care and Pain Medicine, Tan Tock Seng Hospital, Singapore, Singapore. yie.hui.lau@nhghealth.com.sg.
Sanchalika AcharyyaClinical Research & Innovation Office , Tan Tock Seng Hospital, Singapore, Singapore.
Cadence Wei Lin WeeAnaesthesiology, Intensive Care and Pain Medicine, Tan Tock Seng Hospital, Singapore, Singapore.
Huiying XuRespiratory and Critical Care Medicine, Tan Tock Seng Hospital, Singapore, Singapore.
Rafael Pulido SacloloEmergency Medicine, Tan Tock Seng Hospital, Singapore, Singapore.
Kelly CaoClinical Research & Innovation Office , Tan Tock Seng Hospital, Singapore, Singapore.
Wee Kim FongAnaesthesiology, Intensive Care and Pain Medicine, Tan Tock Seng Hospital, Singapore, Singapore.

Funding

Ng Teng Fong Health Innovation Program FY 2023 Pitch-for-Fund grant NTF_FY2023_I_C3_02
6 · The paper itself

Abstract

backgroundFocused cardiac ultrasound (FCU) is increasingly used as an extension of physical examination to aid diagnosis and clinical decision-making. Emerging educational technologies such as artificial intelligence (AI)-enabled ultrasound devices and virtual reality (VR) simulators offer novel, cost-effective and self-directed approaches for FCU skill acquisition training. Prior studies suggest that VR-based training may be non-inferior to traditional teaching, while AI offers real-time feedback to enhance learning.

objectiveThis study aimed to evaluate the effectiveness and non-inferiority of AI and VR-assisted training compared to Traditional in-person instruction in achieving competency in FCU image acquisition. Secondary outcomes included time to acquire an optimal apical 4 chamber (A4C) view and self-reported confidence in image acquisition, assessed immediately post-training and at 3-month follow up.

methodsIn this single-blind, randomized controlled pilot trial, 66 local medical students with no prior FCU experience were randomised into 3 arms: (1) AI-enabled ultrasound training using the Kosmos system, (2) VR-based stimulator (Vimedix), and (3) Traditional instructor-led teaching. All sessions were 60 min long. Image acquisition of 5 standard FCU views was assessed by blinded evaluators using the Rapid Assessment of Competency in Echocardiography (RACE) score at both time points.

resultsTwo participants were lost to follow-up (one each from the AI and VR groups). In the first assessment, the Traditional group achieved the highest mean RACE score (15.77), followed by AI (13.39) and VR (13.23). Non-inferiority testing confirmed that both AI (95% CI -∞ to 3.60; p < 0.001) and VR (95% CI -∞ to 3.58; p < 0.001) methods were non-inferior to Traditional instruction. The AI group achieved the shortest mean time to acquire an optimal A4C view (158 ± 99.1 s), followed by the VR (189 ± 94.7 s), and traditional (199 ± 115.1 s), though differences were not statistically significant (p = 0.591). Confidence levels were initially highest in the Traditional group, while the VR group showed higher confidence at 3-month follow-up, particularly in parasternal long-axis view acquisition.

conclusionsAI and VR-based training methods were non-inferior to traditional instruction for FCU skill acquisition. Both modalities show promise as scalable, technology-enabled alternatives in ultrasound education. Trial registration This trial was registered on Clinicaltrials.gov (NCT06355557).

Indexed as

Artificial intelligenceUltrasoundVirtual reality

Identifiers

PMID41269495
PMCPMC12638561

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Registered trials

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.