{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,27]],"date-time":"2026-08-27T20:52:07Z","timestamp":1787863927190,"version":"build-2784847793"},"reference-count":40,"publisher":"European Respiratory Society (ERS)","issue":"2","license":[{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"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":[[2026,1,30]]},"published-print":{"date-parts":[[2026,4]]},"abstract":"<jats:p>Thoracic ultrasound (TUS) is a key bedside tool for detecting pleural effusion and pneumothorax, offering high sensitivity, portability and radiation-free assessment. However, its reliability is limited by operator dependency and variable training, posing challenges in emergency, intensive care and resource-limited settings. Artificial intelligence (AI) has emerged as a potential adjunct to support TUS interpretation, with deep learning algorithms showing promising accuracy in research studies. Evidence suggests AI may perform well for straightforward cases, yet performance declines significantly during external validation and for complex or low-quality images, precisely where clinical decision support is most needed. Five potential scenarios for AI application are identified: emergency triage, intensive care unit monitoring, post-procedural safety checks, deployment in resource-limited environments, and educational feedback for trainees. Despite these opportunities, current AI systems remain immature: methodological limitations, operator-dependence and lack of real-world outcome data constrain safe clinical adoption. Rigorous prospective trials, multisite validation, standardised reporting, and integration of quality assurance are essential before routine use. At present, AI-assisted TUS should be regarded as a research and educational tool rather than a substitute for clinical judgment. Thoughtful development and cautious implementation are required to transform AI from an experimental promise into a reliable, patient-centred clinical resource.<\/jats:p>","DOI":"10.1183\/20734735.0002-2026","type":"journal-article","created":{"date-parts":[[2026,5,19]],"date-time":"2026-05-19T08:30:21Z","timestamp":1779179421000},"page":"260002","update-policy":"https:\/\/doi.org\/10.1183\/ers-crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["Artificial intelligence for pleural effusion and pneumothorax detection on thoracic ultrasound: an educational viewpoint on the promise, pitfalls and path forward"],"prefix":"10.1183","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-6122-7792","authenticated-orcid":false,"given":"Guido","family":"Marchi","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"81","published-online":{"date-parts":[[2026,5,19]]},"reference":[{"key":"2026051901301431000_22.2.260002.1","doi-asserted-by":"publisher","DOI":"10.1183\/13993003.01519-2020"},{"key":"2026051901301431000_22.2.260002.2","doi-asserted-by":"crossref","first-page":"2062","DOI":"10.3390\/jcm14062062","article-title":"Thoracic ultrasound for pre-procedural dynamic assessment of non-expandable lung: a non-invasive, real-time and multifaceted diagnostic tool","volume":"14","author":"Marchi","year":"2025","journal-title":"J Clin Med"},{"key":"2026051901301431000_22.2.260002.3","doi-asserted-by":"publisher","DOI":"10.1183\/20734735.0053-2023"},{"key":"2026051901301431000_22.2.260002.4","doi-asserted-by":"crossref","first-page":"6326","DOI":"10.3390\/jcm14176326","article-title":"Intercostal artery screening with color doppler thoracic ultrasound in pleural procedures: a potential yet underexplored imaging modality for minimizing iatrogenic bleeding risk in interventional pulmonology","volume":"14","author":"Marchi","year":"2025","journal-title":"J Clin Med"},{"key":"2026051901301431000_22.2.260002.5","doi-asserted-by":"publisher","DOI":"10.1183\/20734735.0282-2025"},{"key":"2026051901301431000_22.2.260002.6","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1055\/a-2702-5310","article-title":"Professional standards in thoracic ultrasound \u2013 EFSUMB position paper","volume":"47","author":"Laursen","year":"2026","journal-title":"Ultraschall Med"},{"key":"2026051901301431000_22.2.260002.7","doi-asserted-by":"publisher","DOI":"10.1183\/23120541.01564-2025"},{"key":"2026051901301431000_22.2.260002.8","doi-asserted-by":"publisher","DOI":"10.1183\/20734735.0256-2024"},{"key":"2026051901301431000_22.2.260002.9","doi-asserted-by":"crossref","first-page":"335","DOI":"10.1159\/000548742","article-title":"Artificial intelligence in interventional pulmonology: promise versus proof","volume":"105","author":"Marchi","year":"2025","journal-title":"Respiration"},{"key":"2026051901301431000_22.2.260002.10","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1007\/s11547-025-02109-5","article-title":"CCM-ITALUNG2 pilot on lung cancer screening in Italy: recruitment, integration with smoking cessation and baseline results","volume":"131","author":"Gorini","year":"2026","journal-title":"Radiol Med"},{"key":"2026051901301431000_22.2.260002.11","first-page":"571","article-title":"Valutazione comparativa di modelli linguistici di grandi dimensioni per il supporto all'educazione sanitaria del paziente con BPCO: Uno studio pneumologico internazionale delle risposte generate da ChatGPT-4, Claude 3.5 Sonnet e Gemini 1.5 Advanced","volume":"116","author":"Marchi","year":"2025","journal-title":"Recenti Prog Med"},{"key":"2026051901301431000_22.2.260002.12","doi-asserted-by":"crossref","first-page":"117","DOI":"10.4103\/atm.atm_192_23","article-title":"Artificial intelligence in respiratory care: current scenario and future perspective","volume":"19","author":"Al-Anazi","year":"2024","journal-title":"Ann Thorac Med"},{"key":"2026051901301431000_22.2.260002.13","doi-asserted-by":"crossref","unstructured":"Marchi G , Fanni SC , Mercier M , et al. 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