ReviewNPJ digital medicine2025
Overcoming barriers in the use of artificial intelligence in point of care ultrasound.
Review in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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.
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.
Who cites it
12 citing papers in PubMed.
- Artificial Intelligence in Point-of-Care Ultrasound: Domains, Barriers and a Framework for Future Development.Journal of the American College of Emergency Physicians open · 2026Article
- The diagnostic accuracy of left ventricular ejection fraction assessment between visual and artificial intelligence-based algorithms on bedside ultrasound.Cardiovascular ultrasound · 2026Article
- Explainable Quality Assessment and Measurement from Real-World Hip Ultrasound Cine Sweeps.Bioengineering (Basel, Switzerland) · 2026Article
- Artificial Intelligence Assistance Does Not Provide Immediate Benefit for Right Upper Quadrant Window Acquisition.Cureus · 2026Article
- A modern radiologist's guide to artificial intelligence.Pediatric radiology · 2026Review
- AI-Enhanced POCUS in Emergency Care.Diagnostics (Basel, Switzerland) · 2026Review
- Gas Vesicles and Acoustic Protein Nanostructures in Molecular Ultrasound Nanomedicine: Translational Archetypes, Biomaterial Design, and Barriers to Clinical Realization.International journal of nanomedicine · 2026Review
- Artificial Intelligence Enabled Lung Sound Auscultation in the Early Diagnosis and Subtyping of Interstitial Lung Disease.Journal of clinical medicine · 2025Review
- Performance and development trends of ultrasound diagnostic systems in military settings: a review.The ultrasound journal · 2025Review
- Clinical obstacles to machine-learning POCUS adoption and system-wide AI implementation (The COMPASS-AI survey).The ultrasound journal · 2025Article
- Artificial intelligence-driven framework for improving prenatal screening for congenital heart disease in rural Nebraska.Frontiers in pediatrics · 2025Article
- Exploring perceptions of data risks in AI-enabled nursing research: A qualitative study.Digital healthArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Point-of-care ultrasound is a portable, low-cost imaging technology focused on answering specific clinical questions in real time. Artificial intelligence amplifies its capabilities by aiding clinicians in the acquisition and interpretation of the images; however, there are growing concerns on its effectiveness and trustworthiness. Here, we address key issues such as population bias, explainability and training of artificial intelligence in this field and propose approaches to ensure clinical effectiveness.
Identifiers
What OpenQuestion holds
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.