Artificial intelligence for pleural effusion and pneumothorax detection on thoracic ultrasound: an educational viewpoint on the promise, pitfalls and path forward.

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Abstract

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.

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