Inteligencia artificial en el diagnóstico de enfermedades respiratorias un enfoque en la detección temprana

Abstract

Artificial intelligence has emerged as a promising tool for the early detection of respiratory diseases through the analysis of medical images, lung sounds and clinical data. Deep learning models enable the identification of subtle patterns in radiographs and CT scans, while signal processing algorithms facilitate the evaluation of cough, wheezing and crackles. The integration of clinical, demographic, behavioural and environmental information enhances the creation of individualised risk profiles and supports preventive strategies. Model interpretability is essential for clinical adoption, ensuring transparency, trust and safety in medical decision-making. Effective implementation also requires consideration of ethical and equity factors, including data protection, bias reduction and accessibility across diverse contexts. Collectively, these advances position artificial intelligence as a complementary resource to improve accuracy, speed and personalisation in respiratory diagnosis, promoting timely interventions and optimising healthcare resource management, with a positive impact on the prevention and control of respiratory diseases.

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