INTELIGENCIA ARTIFICIAL EN EL DIAGNÓSTICO MÉDICO: UN ENFOQUE BASADO EN APRENDIZAJE PROFUNDO

Abstract

The article "Artificial Intelligence in Medical Diagnosis: A Deep Learning Approach" investigates the impact and challenges associated with implementing deep learning models in medical diagnosis. Through a mixed-methods approach, AI models were developed and tested, showing significant improvements in accuracy, sensitivity, and specificity over traditional diagnostic methods. These advancements indicate a substantial potential for treatment personalization and early interventions. However, the study also outlines critical challenges in effectively integrating these technologies into clinical practice, including the need for extensive training data sets, the interpretation of results by medical professionals, and ethical considerations regarding AI use. Overcoming these hurdles necessitates specific strategies, such as medical training on AI technologies and the establishment of robust ethical frameworks. This research emphasizes the need for interdisciplinary collaboration to facilitate AI adoption in medical diagnosis, heralding a revolution in diagnostic efficiency and personalization.