Seven Pillars for a Community-Led AI-POCUS Future – A WINFOCUS Manifesto

Adrian Wong; Julina Noor; Francesco Corradi; Gabriele Via

doi:10.5826/tuj.2026.18454

Authors

Adrian Wong Dept of Intensive Care Medicine, Ng Teng Fong General Hospital, Singapore; Faculty of Medicine, Universiti Malaya, Malaysia https://orcid.org/0000-0003-4968-7328
Julina Noor Dept of Emergency Medicine, Faculty of Medicine, Universiti Teknologi MARA, Kuala Lumpur, Malaysia
Francesco Corradi Dept of Surgical, Medical, Molecular Pathology and Critical Care Medicine, University of Pisa, Pisa, Italy; Azienda Tutela della Salute Liguria, ASL, 5, La Spezia, Italy
Gabriele Via Cardiac Anesthesia and Intensive Care, Ente Ospedaliero Cantonale (EOC), Istituto Cardiocentro Ticino, Università della Svizzera Italiana (USI), Lugano, Switzerland

Keywords:

POCUS, Machine learning (ML), Artificial intelligence (AI), Ultrasound

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.

References

[1] GE HealthCare. "GE HealthCare to Acquire Caption Health." February 9, 2023. https://www.gehealthcare.com/about/newsroom/press-releases/ge-healthcare-to-acquire-caption-health-expanding-ultrasound-to-support-new-users-through-fda-cleared-ai-powered-image-guidance-

[2] UltraSight. "FDA Grants Clearance for UltraSight's AI-Powered Cardiac Ultrasound Technology." July 27, 2023. https://ultrasight.com/fda-grants-clearance-for-ultrasights-ai-powered-cardiac-ultrasound-technology/

[3] Motazedian P, Marbach JA, Prosperi-Porta G et al. Diagnostic accuracy of point-of-care ultrasound with artificial intelligence-assisted assessment of left ventricular ejection fraction. NPJ Digit Med. 2023 Oct 28;6(1):201. doi: 10.1038/s41746-023-00945-1. PMID: 37898711; PMCID: PMC10613290.

[4] Kagiyama, N., Abe, Y., Kusunose, K. et al. Multicenter validation study for automated left ventricular ejection fraction assessment using a handheld ultrasound with artificial intelligence. Sci Rep 14, 15359 (2024). https://doi.org/10.1038/s41598-024-65557-5

[5] Wong, A., Roslan, N.L., McDonald, R. et al. Clinical obstacles to machine-learning POCUS adoption and system-wide AI implementation (The COMPASS-AI survey). Ultrasound J 17, 32 (2025). https://doi.org/10.1186/s13089-025-00436-2

[6] Lekadir, K., et al. 2025. "FUTURE-AI: international consensus guideline for trustworthy and deployable artificial intelligence in healthcare." BMJ 2025; 388 e081554 https://doi.org/10.1136/bmj-2024-081554

[7] Kim S, Fischetti C, Guy M, Hsu E, Fox J, Young SD. Artificial Intelligence (AI) Applications for Point of Care Ultrasound (POCUS) in Low-Resource Settings: A Scoping Review. Diagnostics (Basel). 2024 Aug 1;14(15):1669. doi: 10.3390/diagnostics14151669.

[8] Caroline Jones, James Thornton, Jeremy C Wyatt, Artificial intelligence and clinical decision support: clinicians’ perspectives on trust, trustworthiness, and liability, Medical Law Review, Volume 31, Issue 4, Autumn 2023, Pages 501–520, https://doi.org/10.1093/medlaw/fwad013

[9] Vaccaro M, Almaatouq A, Malone T. When combinations of humans and AI are useful: A systematic review and meta-analysis. Nat Hum Behav. 2024 Dec;8(12):2293-2303. doi: 10.1038/s41562-024-02024-1. Epub 2024 Oct 28.

[10] Chen, M., Wang, Y., Wang, Q. et al. Impact of human and artificial intelligence collaboration on workload reduction in medical image interpretation. Digit. Med. 7, 349 (2024). https://doi.org/10.1038/s41746-024-01328-w