ArticleThe ultrasound journal2026
Seven Pillars for a Community-Led AI-POCUS Future - A WINFOCUS Manifesto.
Article in The ultrasound journal, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The rapid integration of Artificial Intelligence (AI) into Point-of-Care Ultrasound (POCUS) represents a transformative shift, offering the potential to democratize diagnostic expertise while simultaneously presenting significant risks regarding clinical validation, workforce preparedness, and health equity. Informed by a recent global survey indicating that while 81% of practitioners are optimistic about AI, major concerns remain regarding training and evidence gaps, the World Interactive Network Focused On Critical UltraSound (WINFOCUS) proposes a unified strategic framework. This manifesto outlines seven foundational pillars to guide the ethical and effective adoption of AI-augmented POCUS: (1) earning trust through rigorous, prospective evidence; (2) building an AI-literate workforce through evolved curricula; (3) championing global equity to prevent widening health disparities; (4) ensuring algorithmic transparency and accountability; (5) designing for seamless human-AI collaboration; (6) establishing a sustainable, privacy-centric data infrastructure; and (7) committing to continuous, patient-centered evaluation. We present this roadmap as a global call to action for clinicians, researchers, and industry partners to collectively shape a future where technology amplifies clinical wisdom and improves patient outcomes.
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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.