Artificial Intelligence in Medicine: diagnostic, therapeutic and ethical impact

Introduction: artificial intelligence (AI) is reshaping how diseases are diagnosed, treated and monitored, integrating machine learning, deep learning and natural language processing into the analysis of large volumes of clinical data. Objective: to comprehensively analyze the impact of artificial intelligence on medicine, examining its main diagnostic, therapeutic and clinical-management applications, as well as the challenges, limitations and ethical considerations that condition its safe, equitable and patient-centered integration. Development: AI has matured most in medical imaging, oncology and precision medicine, complementing rather than replacing clinical judgment; randomized evidence in cardiology shows improved workflow efficiency and reduced adverse events, including all-cause mortality. Implementation nonetheless faces technical, organizational and social barriers—data quality and interoperability, lack of explainability, algorithmic bias, and gaps in clinician training—alongside ethical and regulatory challenges regarding privacy, accountability and informed consent. Conclusions: AI offers considerable potential to reduce costs, increase diagnostic accuracy, personalize care and expand access to quality services, provided its deployment overcomes technical, ethical and regulatory barriers through proactive, adaptive and patient-centered governance.