{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,13]],"date-time":"2026-10-13T08:54:30Z","timestamp":1791881670870,"version":"4.3.4"},"reference-count":86,"publisher":"European Respiratory Society (ERS)","issue":"4","license":[{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":["publications.ersnet.org"],"crossmark-restriction":true},"short-container-title":["Breathe"],"accepted":{"date-parts":[[2026,7,3]]},"published-print":{"date-parts":[[2026,10]]},"abstract":"<jats:p>Point-of-care ultrasound (POCUS) is an increasingly utilised tool in pulmonary and critical care medicine, yet its effectiveness remains limited by operator dependency and the required cognitive demands of real-time image acquisition and interpretation. Artificial intelligence (AI) has emerged as a promising approach to augment POCUS by supporting image acquisition, interpretation and quantitative analysis.<\/jats:p>\n                  <jats:p>Given the rapid evolution of this field, clinicians must understand both the capabilities and limitations of AI-enabled POCUS. However, prior reviews have largely catalogued applications without focusing on how these tools integrate into bedside workflows. This narrative review examines AI in cardiopulmonary POCUS through a workflow-based lens relevant to pulmonologists and intensivists.<\/jats:p>\n                  <jats:p>We discuss the role of AI as a bedside coach, interpreter and quantifier, highlighting applications in lung and focused cardiac ultrasound, including pneumothorax detection, B-line analysis, left ventricular function assessment, and identification of other cardiopulmonary pathologies. Across domains, AI demonstrates strong performance in controlled settings, although variability persists in real-world environments due to differences in image quality, patient factors and dataset limitations.<\/jats:p>\n                  <jats:p>Future directions include interactive, clinician-in-the-loop systems, protocol-driven integration, and multimodal data synthesis. A clear understanding of these developments will be essential for clinicians as AI becomes increasingly embedded within bedside patient care.<\/jats:p>","DOI":"10.1183\/20734735.0058-2026","type":"journal-article","created":{"date-parts":[[2026,10,13]],"date-time":"2026-10-13T08:30:16Z","timestamp":1791880216000},"page":"260058","update-policy":"https:\/\/doi.org\/10.1183\/ers-crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["AI-augmented cardiopulmonary point-of-care ultrasound: a narrative review for pulmonary and critical care clinicians"],"prefix":"10.1183","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-3762-1176","authenticated-orcid":false,"given":"James","family":"Mattson","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-1516-702X","authenticated-orcid":false,"given":"Kaivalya","family":"Deshpande","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5693-0157","authenticated-orcid":false,"given":"Deepak","family":"Pradhan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"81","published-online":{"date-parts":[[2026,10,13]]},"reference":[{"key":"2026101301300967000_22.4.260058.1","doi-asserted-by":"crossref","first-page":"573","DOI":"10.1016\/j.ccc.2025.02.007","article-title":"Competencies, certification, and credentialing in critical care 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