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 conferencesDr. 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.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.Dr. Shakti Mishra is an Associate Professor and Head of CSE at PDEU. His academic credentials include a Ph.D. and B.Tech, and his expertise spans distributed computing, cloud computing, energy-efficient systems, and machine learning for renewable energy. His publication record includes works on cloud ontology, load balancing, fraud detection, fake-news detection, solar prediction, and smart systems.

• 1. Introduction to Digital Medical Twins – Concept, Evolution, and Healthcare Applications2. Multi-Modal Data for Medical Twins – EHRs, Imaging, Genomics, IoT/Wearables3. Multi-Modal Data Fusion and Representation Learning4. Wearables, IoT, and Remote Monitoring in Medical Twins5. Generative AI Foundations for Medical Twins – LLMs, Diffusion Models6. Building Digital Twins of Organs and Systems7. Personalized Diagnostics and Prognosis with Medical Twins8. Integration of Digital Medical Twins into Smart Hospitals and Healthcare Systems9. Digital Medical Twins for Preventive and Lifestyle Medicine10. Global Health Perspectives and Future Healthcare Delivery Models11. Simulation and Predictive Modeling – Virtual Clinical Trials, Outcome Forecasting12. Ethics, Governance, Transparency, and Implementation Challenges13. Regulatory and Standards Frameworks for Clinical Deployment of Digital Medical Twins14. Emerging Directions – Metaverse, AR/VR, and Immersive Analytics15. Roadmap for the Next Generation of Digital Medical Twins