Keynote Speaker

Prof. Jose Antonio Marmolejo
National Autonomous University of Mexico, Mexico

Jose Antonio Marmolejo is a Professor at National Autonomous University of Mexico, Mexico. His research is on operations research, large-scale optimization techniques, computational techniques and analytical methods for planning, operations, and control of electric energy and logistic systems. He is particularly interested in topics related to artificial intelligence, digital twins, the Internet of things and Industry 4.0. Currently, Prof. Marmolejo, together with his graduate students, leads the laboratory for the development of digital twins in companies and businesses. He received his Ph. D in Operations Research (Hons) at the National Autonomous University of Mexico. At present, he has the first-highest country-wide distinction granted by the Mexican National System of Research Scientists for scientific merit (SNI Fellow, Level 3). He is a member of the Network for Decision Support and Intelligent Optimization of Complex and Large-Scale Systems and Mexican Society for Operations Research. He has co-authored research articles in science citation index journals, conference proceedings, presentations, books, and book chapters.

Abstract:

Digital twins are new technologies that are rapidly transforming the healthcare sector. This technology guarantees best practices in medical organizations and patient care, which benefits the health industry.

Digital twins require several processes, for example sensors are used to acquire and integrate data or signals in real time, including traditional sensors and novel sensors. Information processing requires fast and efficient procedures. Therefore, machine learning models can be developed to solve problems in real time. Model building is a relevant stage in the development of digital twins in healthcare, combining human anatomy and digital technology through image processing, digital collection processing, mathematical modeling and other technologies.

The modular design of digital twins can help speed up development and improve modeling quality. This allows links that take into account interactions between multiple threads. However, the need for further validation and lack of clinical interpretability have limited the achievements of these technologies.

It should be noted that this trend is not only temporary but an absolute change in technological influence in the field of healthcare management. This keynote will explore the importance of digital twins in healthcare, highlighting the benefits, possibilities and challenges.