AI AND DATA SCIENCE IN PRECISION MEDICINE PREDICTIVE ANALYTICS AND MEDICAL PRACTICE (PB 2026)

AI and Data Science in Precision Medicine, Predictive Analytics, and Medical Practice examines the transformative role of AI and data science in improving diagnosis, treatment, and healthcare delivery. It shows how machine learning, deep learning, and advanced signal and image analysis enable breakthroughs in genomics, multi-omics integration, biomedical imaging, EEG-based seizure prediction, and real-time physiological monitoring. The book highlights AI-driven stratification of complex syndromes such as sepsis, stroke, and acute respiratory distress syndrome, demonstrating how data-driven models support early detection, personalized interventions, and actionable clinical decisions.
The volume also presents system-level innovations, including AI-based forecasting for dialysis, blood supply management, and telemedicine optimization. It addresses ethical and regulatory challenges, fairness, transparency, data governance, and clinical validation, providing a practical roadmap for healthcare professionals, engineers, researchers, and policymakers. By integrating responsible, human-centered AI into precision medicine, the book illustrates clear pathways to enhance patient care, improve outcomes, and promote equitable healthcare.

• Explores AI and data science in precision medicine, integrating genomics, imaging, and multi-omics for actionable insights
• Demonstrates predictive analytics across major clinical conditions, offering a technology-driven roadmap to improve care and outcomes
• Highlights ethical, regulatory, and governance considerations essential for responsible AI deployment in healthcare

Olfa Boubaker is a Full Professor at the National Institute of Applied Sciences and Technology (INSAT) at the University of Carthage, Tunisia. Her research spans control theory, nonlinear systems, and robotics, with a focus on healthcare applications and human-centered technologies. She received her PhD in Electrical Engineering from the National Engineering School of Tunis (ENIT) and Habilitation Universitaire degree in Control Engineering from the National Engineering School of Sfax (ENIS), in Tunisia. Professor Boubaker leads interdisciplinary research projects at the interface of medicine and technology and serves as Series Editor of Medical Robots and Devices: New Developments and Advances. She has authored over 150 peer-reviewed papers and several books, and is an Associate Editor for Robotica and the International Journal of Advanced Robotic Systems. She also contributes to various scientific journals and mentors numerous engineering graduates.

Mohamed Boussarsar is Professor of Intensive Care Medicine at the Faculty of Medicine of Sousse, University of Sousse, Tunisia, and Head of the Medical Intensive Care Unit at Farhat Hached University Hospital. His clinical and research interests focus on acute respiratory failure, with a particular emphasis on the optimization of non-invasive respiratory support (NIRS) and invasive mechanical ventilation (IMV). His work integrates a frugality-driven approach, aiming to adapt evidence-based practices and clinical guidelines to the realities of low- and middle-income countries.

Professor Boussarsar has conducted numerous studies to refine diagnostic strategies, predict outcomes, and improve interventions in critical care. He coordinates a long-standing NIRS master program, covering acute and chronic respiratory support, and organizes two prominent annual conferences: @-VAC (mechanical ventilation) and ICCPC (respiratory diseases). He has also led biomedical engineering innovations during the COVID-19 pandemic, including the development of ventilators and HFNC devices.

In 2023, he launched Tunisia’s first post-graduate course on AI applied to healthcare, promoting AI-driven precision medicine and translational research. Professor Boussarsar is an active member of several national and international scientific societies and serves as a reviewer and editorial board member for leading journals in intensive care and pulmonology.