AI and Data Science in Precision Medicine, Predictive Analytics, and Medical Practice
- 1st Edition - May 1, 2026
- Latest edition
- Editors: Olfa Boubaker, Mohamed Boussarsar
- Language: English
AI and Data Science in Precision Medicine, Predictive Analytics, and Medical Practice examines the transformative role of AI and data science in improving diagnosis, treatm… Read more
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.
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.
Data scientists and engineers, researchers and academics in the interconnected fields of artificial intelligence and data science in healthcare
Introduction to AI and Data Science in Precision Medicine, Predictive Analytics and Clinical Applications
Part I: Foundations of AI, Data Science, and Medical Training in Precision Medicine
1. Artificial Intelligence-Driven Personalized Medicine
2. AI Revolution in Healthcare: Enhancing Patient Care and Outcomes through Innovative Applications and Future Prospects
3. Precision medicine, omics, and treatable traits as a paradigm shift towards promising medical curriculum
Part II: Core Perspectives on AI in Precision Medicine: Genomics, Imaging, and Drug Discovery
4. Distributed Deep Learning Approaches for Genomics Analysis: A Comprehensive Review
5. Drug Discovery and Development: Leveraging AI and data science to accelerate the discovery of novel therapeutic compounds and optimize drug development pipelines
6. Advancements in Medical Imaging: Harnessing AI for Early Disease Detection and Diagnosis
Part III: Data-Driven AI Techniques for Diagnosis, Prediction, and Personalized Treatment
7. AI-Based Seizure Prediction Using EEG Signal to Image Conversion Techniques
8. CNN-Based Retinal Disease Classification Using OCT Imaging and EfficientNetB09. ML-based Prognostic models for hypertensive acute response for stroke patients in intensive care units and emergency departments
10. Precision Medicine in Acute Respiratory Distress Syndrome (ARDS)
11. Sepsis and precision medicine: Tailoring interventions using patient-specific data and biomarkers
Part IV: AI-Driven Optimization of Healthcare Systems and Medical Logistics
12. Enhancing Telemedicine and Remote Patient Monitoring with AI and Data Science
13. Enhancing Peritoneal Dialysis Care: Leveraging Predictive Analytics and AI
14. AI-Driven Blood Supply Chain Management: A Reinforcement Learning Approach
Part V: Emerging Trends, Ethical Challenges, and Future Perspectives in AI-driven Healthcare
15. Chaos Theory in AI-Driven Healthcare Systems: Unraveling Complex Data Patterns
16. Ethical Challenges of Artificial Intelligence in Critical Care
17. Data-Driven Decision-Making in Healthcare: Unlocking Value through Web Analytics
18. Implementing Responsible Artificial Intelligence-driven Precision Medicine in Critical Care
19. Enrichment Strategies in Precision Medicine for Future Clinical Trials
20. Conclusions in Precision Médecine and Predictive Analytics
- Edition: 1
- Latest edition
- Published: May 1, 2026
- Language: English
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Olfa Boubaker
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.
Affiliations and expertise
Full Professor, National Institute of Applied Sciences and Technology (INSAT), University of Carthage, TunisiaMB
Mohamed Boussarsar
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.
Affiliations and expertise
Professor of Intensive Care Medicine, Faculty of Medicine of Sousse, University of Sousse, Tunisia