Human health is a fundamental pillar of individual and collective well-being, as it determines people’s ability to reach their potential, contribute to social progress and carry out daily activities. According to the World Health Organization (WHO), health implies not only the absence of disease, but also a complete state of physical, mental and social well-being. Respiratory diseases such as influenza, the common cold, COPD, asthma, pneumonia and allergic rhinitis significantly impact human health by compromising respiratory function, generating acute symptoms and causing chronic complications. These conditions reduce physical capacity, impair quality of life and generate socioeconomic burdens. Early and accurate diagnosis is essential to mitigate their impact, as diseases such as COPD affect more than 200 million people worldwide. However, challenges such as limited access to medical care, unspecified symptoms, continuous exposure to risk factors and delays in referral to specialized centers still persist. In this context, artificial intelligence (AI) presents itself as a key ally for early diagnosis, improving clinical accuracy and optimizing time-consuming tasks. In response to these challenges, SIDTER is presented: a prototype AI-assisted early diagnosis system for respiratory diseases with medical supervision and validation. It aims to support physicians, improve clinical diagnostic capabilities and strengthen patient-physician interaction.