Assessing the Efficiency and Ergonomic Benefits of Artificial Intelligence–Assisted Acquisition

Objective: The aim of this study was to evaluate the effect of artificial intelligence (AI) assistance on examination efficiency and ergonomics during cardiac sonography. Materials and Methods: Randomized, cross-over study design was used with an ultrasound equipment system, cardiovascular software, and integrated AI features. A cohort of experienced sonographers (N = 30) performed standardized examinations with and without AI features enabled. All participating sonographers were new equipment users. Metrics of timing with and without AI assistance were acquired. Movements of the non-scanning hand were recorded during a subset of studies to assess time spent in a predefined ergonomic zone. Results: Artificial intelligence assistance reduced mean examination time by 10.16 ± 0.95 minutes (95% CI, 12.10-8.22; P < .001) independent of experience, age, scanning sequence, or prior AI familiarity. A sub-analysis of hand motion during sonography (n = 9) found AI assistance reduced average distance traveled by 50% (47.9 m) compared with unassisted examinations (SD, 10.6; 95% CI, 39.74-55.97, P < .01). Conclusion: In this cohort of users, AI-assisted cardiac sonography significantly reduced acquisition time and increased time spent within their optimal hand reach. This kind of upgrade may help lower the risk of work-related musculoskeletal injuries in cardiac sonographers. Further studies, based on actual clinical settings, would help to further clarify these benefits.

Authors

Institutions

Publication Details

Journal
Journal of diagnostic medical sonography
Published
2026-10-09
DOI
https://doi.org/10.1177/87564793261489172
Primary Topic
Artificial Intelligence in Healthcare and Education
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Assessing the Efficiency and Ergonomic Benefits of Artificial Intelligence–Assisted Acquisition

LS. Lissa Sugeng, Neil J. Weissman, Steve Walling, Alicia Armour
Journal of diagnostic medical sonography
Artificial Intelligence in Healthcare and Education
article

Assessing the Efficiency and Ergonomic Benefits of Artificial Intelligence–Assisted Acquisition

LS. Lissa Sugeng, Neil J. Weissman, Steve Walling, Alicia Armour
article en

Abstract

Objective: The aim of this study was to evaluate the effect of artificial intelligence (AI) assistance on examination efficiency and ergonomics during cardiac sonography. Materials and Methods: Randomized, cross-over study design was used with an ultrasound equipment system, cardiovascular software, and integrated AI features. A cohort of experienced sonographers (N = 30) performed standardized examinations with and without AI features enabled. All participating sonographers were new equipment users. Metrics of timing with and without AI assistance were acquired. Movements of the non-scanning hand were recorded during a subset of studies to assess time spent in a predefined ergonomic zone. Results: Artificial intelligence assistance reduced mean examination time by 10.16 ± 0.95 minutes (95% CI, 12.10-8.22; P < .001) independent of experience, age, scanning sequence, or prior AI familiarity. A sub-analysis of hand motion during sonography (n = 9) found AI assistance reduced average distance traveled by 50% (47.9 m) compared with unassisted examinations (SD, 10.6; 95% CI, 39.74-55.97, P < .01). Conclusion: In this cohort of users, AI-assisted cardiac sonography significantly reduced acquisition time and increased time spent within their optimal hand reach. This kind of upgrade may help lower the risk of work-related musculoskeletal injuries in cardiac sonographers. Further studies, based on actual clinical settings, would help to further clarify these benefits.

Journal of diagnostic medical sonography
Duke University (US), Yale University (US), Saint Francis Hospital & Medical Center (US), MedStar Health (US)
Openalex Percentile: Top 20%
Artificial Intelligence in Healthcare and Education
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Assessing the Efficiency and Ergonomic Benefits of Artificial Intelligence–Assisted Acquisition — LS. Lissa Sugeng, Neil J. Weissman, et al. · Journal of diagnostic medical sonography (2026) | TGRS Research Map | TGRS