: Artificial Intelligence (AI), software and algorithms that analyzes medical data, has been used for diagnosis, therapeutic, and predictive models. AI's optimal use is still controversial. From privacy concerns to accuracy and validity of algorithms and their results are debatable. Dr. Rueda will present examples of successful use of AI in healthcare and explore the potential benefits of this technology, focusing on optimization of available data, early adoption and potential decrease of human error analyzing large amounts of complex data. Dr. Alfonso will focus on the limitations of data privacy and the challenges with the validations of the algorithms used in AI, which could be seen as a "black box" for the user, and the potential lack of accountability from developers versus the healthcare provider or decision maker who is using them. The session will be moderated by Dr. Herran who will highlight critical points and encourage participation from the audience.


: Increased availability of electronic medical information and higher computing power led IT developers and healthcare researchers to combine efforts to develop software and algorithms to identify and predict patterns using complex medical data, known as AI. The use and implementation of AI in healthcare is variable and controversial. The algorithms and software can be proprietary and not fully transparent to the users, limiting the ability to reproduce it's findings or standardize its approaches. The quality and quantity of the data become critical elements for the predictive power of AI but is unclear how to control potential data privacy issues and incomplete or inaccurate data that often lingers in EHRs. In other fields, AI has been transformational but the role in healthcare is still to be determined. Early successes and clear identification of the challenges should help us to enhance its use and inform better decisions to improve the population health.