Artificial intelligence and machine learning: present and future applications in health sciences

Introduction: artificial intelligence and machine learning have brought significant changes and transformed everyday life, and this is also seen in healthcare and medicine. A bibliographic review was carried out with the aim of delving into the current and future applications of artificial intelligence and machine learning in the health and biomedical sciences sector. Methods: a bibliographic review was carried out in the main databases and other search services. The terms “artificial intelligence”, “automated learning”, “deep learning”, “health sciences” were used, as well as search descriptors. Results: artificial intelligence (AI) models are playing an increasingly important role in biomedical research and clinical practice, showing their potential in various applications, such as risk modeling and stratification, personalized screening, diagnosis (including classification of molecular disease subtypes), prediction of response to therapy, and prognosis. All of these fields of research could greatly improve the current trend towards precision medicine, resulting in more reliable and personalized approaches with a high impact on diagnostic and therapeutic pathways. This implies a paradigm shift from defining statistical and population perspectives to individual predictions, allowing for more effective preventive actions and therapy planning. Conclusions: there is high potential for the application of artificial intelligence and machine learning on a large scale in the future