ArticleDiagnostics (Basel, Switzerland)2024
Artificial Intelligence (AI) Applications for Point of Care Ultrasound (POCUS) in Low-Resource Settings: A Scoping Review.
Article in Diagnostics (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers.
What it found
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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
39 citing papers in PubMed.
- Phantom-based training of ultrasound-guided breast biopsy in medical education: a randomized controlled trial comparing handheld and high-end ultrasound.BMC medical education · 2025Trial
- 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
- Seven Pillars for a Community-Led AI-POCUS Future - A WINFOCUS Manifesto.The ultrasound journal · 2026Article
- Pre-hospital and emergency department point-of-care ultrasound in cardiovascular emergencies: a scoping review.Cardiovascular diagnosis and therapy · 2026Review
- Ultrasound imaging in aesthetic medicine: safety and precision in injectable procedures.Journal of ultrasound · 2026Review
- Point-of-care ultrasound in prehospital emergency care: attitudes, facilitators and barriers.BMC medical education · 2026Article
- [Research progress in artificial intelligence for the diagnosis and management of diseases in preterm infants].Zhongguo dang dai er ke za zhi = Chinese journal of contemporary pediatrics · 2026Review
- AI-based quality control was associated with improved fetal ultrasound image quality in low-resource settings: a real-world multicenter study from West China.BMC medicine · 2026Observational
- Reimbursement and Policy Considerations of Point-of-Care Ultrasound (POCUS) in Rural Family Medicine.Journal of the American Board of Family Medicine : JABFM · 2026Article
- Diagnostic Challenges and Modern Therapeutic Strategies in Giant Cell Arteritis.Diagnostics (Basel, Switzerland) · 2026Review
- Transforming Public Health Practice with Artificial Intelligence: A Framework-Driven Approach.Healthcare (Basel, Switzerland) · 2026Article
- The evolution and future of point of care ultrasound in the perioperative period: narrative review.Journal of clinical monitoring and computing · 2026Review
- AI-Enhanced POCUS in Emergency Care.Diagnostics (Basel, Switzerland) · 2026Review
- Ultrasound medicine in the era of precision theranostics: Mechanisms, molecular strategies, and clinical translation.Iranian journal of basic medical sciences · 2026Review
- Development of a deep learning model for intussusception using point-of-care ultrasound.Frontiers in radiology · 2026Article
- Rapid bedside multimodal ultrasound screening for acute type A aortic dissection: a perspective.Frontiers in cardiovascular medicine · 2026Article
- Artificial Intelligence-Empowered "Walking Hospitals": A Narrative Review of Innovative Models and Ecosystem Construction in Rural Healthcare.International journal of general medicine · 2026Review
- Article
- Advancing Ultrasound Beamforming With Deep Learning: A Comprehensive Review of Methods, Datasets, Benchmarks, and Computational Challenges.International journal of biomedical imaging · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Advancements in artificial intelligence (AI) for point-of-care ultrasound (POCUS) have ushered in new possibilities for medical diagnostics in low-resource settings. This review explores the current landscape of AI applications in POCUS across these environments, analyzing studies sourced from three databases-SCOPUS, PUBMED, and Google Scholars. Initially, 1196 records were identified, of which 1167 articles were excluded after a two-stage screening, leaving 29 unique studies for review. The majority of studies focused on deep learning algorithms to facilitate POCUS operations and interpretation in resource-constrained settings. Various types of low-resource settings were targeted, with a significant emphasis on low- and middle-income countries (LMICs), rural/remote areas, and emergency contexts. Notable limitations identified include challenges in generalizability, dataset availability, regional disparities in research, patient compliance, and ethical considerations. Additionally, the lack of standardization in POCUS devices, protocols, and algorithms emerged as a significant barrier to AI implementation. The diversity of POCUS AI applications in different domains (e.g., lung, hip, heart, etc.) illustrates the challenges of having to tailor to the specific needs of each application. By separating out the analysis by application area, researchers will better understand the distinct impacts and limitations of AI, aligning research and development efforts with the unique characteristics of each clinical condition. Despite these challenges, POCUS AI systems show promise in bridging gaps in healthcare delivery by aiding clinicians in low-resource settings. Future research endeavors should prioritize addressing the gaps identified in this review to enhance the feasibility and effectiveness of POCUS AI applications to improve healthcare outcomes in resource-constrained environments.
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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.