Artificial Intelligence in Clinical Medicine: Applications in Early Diagnosis, Prognosis, and Support for Medical Decision-Making

Abstract

Introduction: Artificial intelligence (AI) has established itself as one of the most promising technologies for transforming contemporary medicine. The development of machine learning and deep learning algorithms has enabled the analysis of large volumes of clinical data, contributing to improvements in the early diagnosis of diseases, risk stratification, and support for clinical decision-making. Objective: To critically analyze the scientific evidence regarding the application of artificial intelligence in medical practice, with an emphasis on assisted diagnostic systems, predictive prognostic models, and clinical decision support tools. Methodology: A systematic literature review was conducted in the PubMed, Scopus, and Web of Science databases, considering publications between 2018 and 2025. The search strategy used descriptors related to artificial intelligence and medical practice, combined with Boolean operators AND and OR. Results: The evidence demonstrates that artificial intelligence algorithms have a high capacity to identify complex clinical patterns and assist in the early diagnosis of various conditions, including sepsis, cardiovascular diseases, and cardiac arrhythmias. Furthermore, predictive models based on machine learning showed significant performance in predicting hospital mortality, clinical deterioration, and hospital readmissions. Conclusion: Artificial intelligence has high potential to improve medical practice, contributing to the development of more precise, personalized, and data-driven medicine. However, challenges related to the interpretability of algorithms, clinical validation, and data security still need to be overcome for a broad and safe implementation of these technologies.