COJuan Gabriel Cendales, director ejecutivo de LaCardio, discusses how artificial intelligence (AI) is transforming healthcare services across four main areas: diagnosis and risk stratification, operational optimization, clinical decision support, and future personalized medicine through genomic data integration. He highlights the role of machine learning algorithms in analyzing medical images, physiological signals, and clinical data to improve diagnostic accuracy and efficiency. Additionally, predictive models aid in managing hospital resources such as bed allocation and surgical waiting lists. Generative AI systems assist in creating clinical notes and early warning alerts, allowing more time for patient interaction. However, Cendales emphasizes that the challenges surrounding AI adoption are not technological but related to governance, including clinical validation, algorithmic bias management, and data privacy. Before implementing AI, healthcare institutions must follow a structured process involving formal evaluation, data quality checks, risk classification, and testing in isolated environments. Sustainable financial planning and clear criteria for adopting or discontinuing AI
Bias read (Center): The article focuses on the application of AI in healthcare, discussing technical advancements and implementation processes without taking a stance on political issues. It provides a balanced overview of opportunities and challenges associated with AI in the medical field.
