CROSS-MODAL AI FOR BIOMEDICAL FUSION: CHALLENGES AND FUTURE DIRECTIONS 

We are pleased to invite high-quality original book chapter proposals for the upcoming edited volume to be published by Wiley Scrivener titled NCROSS-MODAL AI FOR BIOMEDICAL FUSION: CHALLENGES AND FUTURE DIRECTIONS

Cross-Modal AI for Biomedical Fusion: Challenges and Future Directions present a comprehensive exploration of intelligent computational frameworks for integrating heterogeneous biomedical data using cross-modal artificial intelligence. The book focuses on the convergence of multimodal learning, computational intelligence, cloud computing, Internet of Things (IoT), big data analytics, and trustworthy AI to address complex healthcare challenges. By combining diverse biomedical data sources—including medical imaging, electronic health records (EHRs), genomic and omics data, physiological signals, wearable IoT sensor data, and clinical text—the book demonstrates how cross-modal AI enables accurate disease diagnosis, predictive analytics, personalized medicine, and intelligent clinical decision support.

The volume emphasizes the role of cloud-IoT infrastructures in supporting scalable storage, processing, and real-time analytics of massive biomedical datasets generated by connected healthcare systems and smart medical devices. It highlights advanced computational approaches such as multimodal deep learning, transformer architectures, graph neural networks, knowledge graphs, federated learning, explainable AI (XAI), generative AI, and privacy-preserving machine learning for developing intelligent, reliable, and secure healthcare solutions. Particular attention is given to handling heterogeneous data integration, missing modalities, interoperability, edge-cloud collaboration, and trustworthy AI for clinical applications. The book further explores intelligent analytics and reasoning techniques that facilitate continuous patient monitoring, remote healthcare, digital health ecosystems, smart hospitals, and IoT-enabled biomedical environments. It discusses scalable cloud-based architectures, intelligent data management, semantic knowledge engineering, automated reasoning, and decision support systems that transform raw biomedical data into actionable clinical insights while ensuring transparency, robustness, fairness, and regulatory compliance.

Indexing* : Scopus 

NOTE : ALL THE SUBMISSION MUST BE ORIGINAL AND NOT SUBMITTED TO ANY CONFERENCE/ JOURNAL

📑 Table of Contents (TOC)

Part I: Foundations of Cross-Modal Biomedical AI

1. Chapter 1. Introduction to Cross-Modal Artificial Intelligence in Healthcare

2. Chapter 2. Biomedical Data Modalities: Medical Imaging, Electronic Health Records, Genomics, Wearables, and Clinical Text

3. Chapter 3. Cloud-IoT Infrastructure for Intelligent Biomedical Data Management

Part II: Cross-Modal Learning and Computational Intelligence

4. Chapter 4. Multimodal Deep Learning Architectures for Biomedical Fusion

5. Chapter 5. Transformer Models and Foundation Models for Cross-Modal Healthcare Analytics

6. Chapter 6. Knowledge Graphs, Ontologies, and Semantic Data Integration

7. Chapter 7. Graph Neural Networks and Representation Learning for Biomedical Intelligence

Part III: Intelligent Analytics and Decision Support

8. Chapter 8. Big Data Analytics for Cross-Modal Biomedical Systems

9. Chapter 9. Explainable, Trustworthy, and Responsible AI in Healthcare

10. Chapter 10. AI-Driven Clinical Decision Support Systems

11. Chapter 11. Personalized Medicine through Cross-Modal Data Fusion

Part IV: Cloud-IoT Enabled Smart Healthcare

12. Chapter 12. IoT, Wearable Devices, and Remote Patient Monitoring

13. Chapter 13. Edge-Cloud Computing for Real-Time Healthcare Intelligence

14. Chapter 14. Federated Learning and Privacy-Preserving Cross-Modal AI

15. Chapter 15. Cybersecurity, Data Governance, and Regulatory Compliance in Biomedical AI

Part V: Emerging Applications and Future Directions

16. Chapter 16. Cross-Modal AI for Disease Diagnosis, Prognosis, and Precision Medicine

17. Chapter 17. Generative AI and Digital Twins in Biomedical Research

18. Chapter 18. Challenges in Data Heterogeneity, Missing Modalities, and Model Generalization

19. Chapter 19. Sustainable AI, Ethical Considerations, and Responsible Healthcare Systems

20. Chapter 20. Future Directions in Cross-Modal Biomedical Fusion and Intelligent Healthcare

👨‍🏫 Editor(S)

Dr. Nitish Pathak

Associate Professor,

Bhagwan Parshuram Institute of Technology (BPIT), PSP-4, Dr. KN Katju Marg, Sector17, Rohini, New Delhi, Delhi 110089

nitishpathak2812@gmail.com

Dr. Neelam Sharma

Associate Professor,

Maharaja Agrasen Institute of Technology (MAIT), Rohini, Plot No 1, Sector 22, PSP Area, Delhi, 110086

neelamsharma@mait.ac.in

Dr. Moolchand Sharma

Assistant Professor

Maharaja Agrasen Institute of Technology (MAIT), Rohini, Plot No 1, Sector 22, PSP Area, Delhi, 110086

neelamsharma@mait.ac.in

Dr. Dac-Nhuong Le

Faculty of Information Technology, Haiphong University, Haiphong 180000, Vietnam

📅 Important Dates

• Abstract Submission Deadline: August 30th, 2026

• Full Chapter Submission: November 30th, 2026

• Final Manuscript Submission: January 30th, 2027

All Chapter Submissions must be made through the Microsoft CMT portal 

The Microsoft CMT service was used for managing the peer-reviewing process for this conference. 

This service was provided for free by Microsoft and they bore all expenses, including costs for Azure cloud services as well as for software development and support.