Artificial intelligence for pleural effusion and pneumothorax detection on thoracic ultrasound: an educational viewpoint on the promise, pitfalls and path forward.
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...
| Publicado en: | Breathe Vol. 22; no. 2; pp. 1 - 7 |
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| Autor principal: | |
| Formato: | tables/charts Journal Article |
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European Respiratory Society
Apr2026
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=193889990&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 193889990 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 18106838 85SQ jtl: Breathe issn: 18106838 maglogo: N pubinfo: dt: Apr2026 vid: 22 iid: 2 pid: 76609 pub: European Respiratory Society artinfo: ui: 193889990 193889990 193889990 10.1183/20734735.0002-2026 193889990 ppf: 1 ppct: 6 formats: fmt: @attributes: type: P tig: atl: Artificial intelligence for pleural effusion and pneumothorax detection on thoracic ultrasound: an educational viewpoint on the promise, pitfalls and path forward. aug: au: Marchi, Guido affil: Pulmonology Unit, Cardiothoracic and Vascular Department, University Hospital of Pisa, Pisa, Italy sug: subj: Artificial Intelligence Pleural Effusion Diagnosis Pneumothorax Diagnosis Thorax Ultrasonography Deep Learning Thoracic Diseases Respiratory Tract Diseases Clinical Information Systems Decision Support Systems, Clinical Education, Medical Sensitivity and Specificity ab: 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. pubtype: Academic Journal doctype: tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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