Digital Medical Twins for Personalized Healthcare
Generative AI and Multi-Modal Approaches for Predictive Diagnostics and Precision Medicine
- 1st Edition - February 1, 2027
- Latest edition
- Editors: Yogesh Kumar, Apeksha Koul, Nandini Modi, Shakti Mishra
- Language: English
Digital Medical Twins for Personalized Healthcare: Generative AI and Multi-Modal Approaches for Predictive Diagnostics and Precision Medicine presents a rigorous, evidence-base… Read more
Description
Description
Key features
Key features
- Provides foundational concepts and practical guidance for digital medical twins
- Integrates generative and multi-modal AI to enhance patient data modeling
- Addresses explainability, privacy, and regulatory alignment for clinical data use
- Offers cross-disciplinary case studies across cardiology, oncology, neurology, and preventive care
Readership
Readership
Table of contents
Table of contents
2. Multi-Modal Data for Medical Twins – EHRs, Imaging, Genomics, IoT/Wearables
3. Multi-Modal Data Fusion and Representation Learning
4. Wearables, IoT, and Remote Monitoring in Medical Twins
5. Generative AI Foundations for Medical Twins – LLMs, Diffusion Models
6. Building Digital Twins of Organs and Systems
7. Personalized Diagnostics and Prognosis with Medical Twins
8. Integration of Digital Medical Twins into Smart Hospitals and Healthcare Systems
9. Digital Medical Twins for Preventive and Lifestyle Medicine
10. Global Health Perspectives and Future Healthcare Delivery Models
11. Simulation and Predictive Modeling – Virtual Clinical Trials, Outcome Forecasting
12. Ethics, Governance, Transparency, and Implementation Challenges
13. Regulatory and Standards Frameworks for Clinical Deployment of Digital Medical Twins
14. Emerging Directions – Metaverse, AR/VR, and Immersive Analytics
15. Roadmap for the Next Generation of Digital Medical Twins
Product details
Product details
- Edition: 1
- Latest edition
- Published: February 1, 2027
- Language: English
About the editors
About the editors
YK
Yogesh Kumar
Dr. Yogesh Kumar is an Assistant Professor in the CSE Department at PDEU, holding a Ph.D., M.Tech, and B.Tech in Computer Science and Engineering. He is a distinguished expert in Artificial Intelligence applications in healthcare with over a decade of experience. His work spans deep learning–based disease detection, predictive modeling for chronic illnesses, medical imaging analytics, and ethical AI deployment—recognised among the top 2% of global scientists by Stanford University in both 2023 and 2024. He has authored more than 200 publications (including over 100 Web of Science–indexed), led national consultancy and funded research projects, and played key roles in editorial and peer-review capacities for top-tier journals and conferences
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Apeksha Koul
Dr. Apeksha Koul earned her B.E. in Computer Science & Engineering (Savitribai Phule Pune University), M.Tech (Shri Mata Vaishno Devi University), and Ph.D. (Punjab University, Patiala). She specializes in AI and machine learning, particularly in medical image analysis and diagnostics. Her recent contributions include advanced CNN models for gastric cancer diagnosis, deep learning for airway disease detection, and automated detection systems using chewable food items—all in collaboration with leading researchers. Dr. Koul is known for her adaptive leadership in research and teaching, and her ability to optimize interdisciplinary projects with effective communication and resource management.
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Nandini Modi
Dr. Nandini Modi is an Assistant Professor at PDEU with a Ph.D. focused on eye-gaze tracking in human–computer interaction. She is an IEEE member and her research interests encompass computer vision, cognitive computing, sentiment analysis, and smart healthcare solutions. She has contributed to various international conferences and journals and holds intellectual property rights for several innovations. Dr. Modi also serves as a reviewer for reputed journals and is active in professional communities.
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