{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,4]],"date-time":"2026-08-04T17:04:34Z","timestamp":1785863074661,"version":"3.56.0"},"reference-count":10,"publisher":"Mattioli1885","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.5826\/tuj.2026.18454","type":"journal-article","created":{"date-parts":[[2026,8,4]],"date-time":"2026-08-04T16:03:42Z","timestamp":1785859422000},"page":"18454","source":"Crossref","is-referenced-by-count":0,"title":["Seven Pillars for a Community-Led AI-POCUS Future \u2013 A WINFOCUS Manifesto"],"prefix":"10.5826","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4968-7328","authenticated-orcid":false,"given":"Adrian","family":"Wong","sequence":"first","affiliation":[{"name":"Dept of Intensive Care Medicine, Ng Teng Fong General Hospital, Singapore; Faculty of Medicine, Universiti Malaya, Malaysia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Julina","family":"Noor","sequence":"first","affiliation":[{"name":"Dept of Emergency Medicine, Faculty of Medicine, Universiti Teknologi MARA, Kuala Lumpur, Malaysia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Francesco","family":"Corradi","sequence":"first","affiliation":[{"name":"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"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gabriele","family":"Via","sequence":"first","affiliation":[{"name":"Cardiac Anesthesia and Intensive Care, Ente Ospedaliero Cantonale (EOC), Istituto Cardiocentro Ticino, Universit\u00e0 della Svizzera Italiana (USI), Lugano, Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"27750","published-online":{"date-parts":[[2026,8,4]]},"reference":[{"key":"10.5826\/tuj.2026.18454_ref1","unstructured":"[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-"},{"key":"10.5826\/tuj.2026.18454_ref2","unstructured":"[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\/"},{"key":"10.5826\/tuj.2026.18454_ref3","doi-asserted-by":"crossref","unstructured":"[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.","DOI":"10.1038\/s41746-023-00945-1"},{"key":"10.5826\/tuj.2026.18454_ref4","doi-asserted-by":"crossref","unstructured":"[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","DOI":"10.1038\/s41598-024-65557-5"},{"key":"10.5826\/tuj.2026.18454_ref5","doi-asserted-by":"crossref","unstructured":"[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","DOI":"10.1186\/s13089-025-00436-2"},{"key":"10.5826\/tuj.2026.18454_ref6","doi-asserted-by":"crossref","unstructured":"[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","DOI":"10.1136\/bmj-2024-081554"},{"key":"10.5826\/tuj.2026.18454_ref7","doi-asserted-by":"crossref","unstructured":"[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.","DOI":"10.3390\/diagnostics14151669"},{"key":"10.5826\/tuj.2026.18454_ref8","doi-asserted-by":"crossref","unstructured":"[8] Caroline Jones, James Thornton, Jeremy C Wyatt, Artificial intelligence and clinical decision support: clinicians\u2019 perspectives on trust, trustworthiness, and liability, Medical Law Review, Volume 31, Issue 4, Autumn 2023, Pages 501\u2013520, https:\/\/doi.org\/10.1093\/medlaw\/fwad013","DOI":"10.1093\/medlaw\/fwad013"},{"key":"10.5826\/tuj.2026.18454_ref9","doi-asserted-by":"crossref","unstructured":"[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.","DOI":"10.1038\/s41562-024-02024-1"},{"key":"10.5826\/tuj.2026.18454_ref10","doi-asserted-by":"crossref","unstructured":"[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","DOI":"10.1038\/s41746-024-01328-w"}],"container-title":["The Ultrasound Journal"],"original-title":[],"link":[{"URL":"https:\/\/mattioli1885journals.com\/index.php\/theultrasoundjournal\/article\/download\/18454\/13302","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/mattioli1885journals.com\/index.php\/theultrasoundjournal\/article\/download\/18454\/13302","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,8,4]],"date-time":"2026-08-04T16:03:44Z","timestamp":1785859424000},"score":1,"resource":{"primary":{"URL":"https:\/\/mattioli1885journals.com\/index.php\/theultrasoundjournal\/article\/view\/18454"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8,4]]},"references-count":10,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026]]}},"URL":"https:\/\/doi.org\/10.5826\/tuj.2026.18454","relation":{"has-manifestation":[{"id-type":"other","id":"26-1020-18454-g13302","asserted-by":"subject"}],"is-part-of":[{"id-type":"other","id":"26-1020","asserted-by":"subject"}]},"ISSN":["2524-8987"],"issn-type":[{"value":"2524-8987","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,8,4]]},"article-number":"1"}}