Multimodal Data Fusion in Healthcare
AI Approaches for Precision Diagnosis
- 1st Edition - March 15, 2026
- Latest edition
- Editors: Akansha Singh, Anuradha Dhull, Monika Lamba, Krishna Kant Singh
- Language: English
Multimodal Data Fusion in Healthcare: AI Approaches for Precision Diagnosis explores the transformative potential of AI in modern medicine by integrating diverse data source… Read more
Multimodal Data Fusion in Healthcare: AI Approaches for Precision Diagnosis explores the transformative potential of AI in modern medicine by integrating diverse data sources such as medical imaging, genomics, EHRs, and wearable sensors. It highlights how AI technologies are revolutionizing healthcare systems through personalized and proactive diagnostics. The book covers cutting-edge methodologies, real-world applications, and the challenges of multimodal data fusion. Topics include AI-driven diagnostics, precision medicine, real-time patient monitoring, and the integration of clinical, genomic, and wearable data, providing both theoretical foundations and practical insights. This book is essential for healthcare professionals, data scientists, and engineers, offering clear frameworks for integrating diverse data types. It addresses crucial issues like data interoperability, privacy, and technical constraints, providing practical solutions. It serves as an invaluable reference for understanding and applying AI advancements in diagnostic precision and personalized medicine.
- Explores cutting-edge AI methodologies and their real-world applications in healthcare diagnostics
- Provides comprehensive frameworks for integrating multimodal data, including medical imaging, genomics, EHRs, and wearable sensors
- Addresses critical issues such as data interoperability, privacy, and technical constraints, offering practical solutions for academic and clinical settings
Healthcare professionals, data scientists, biomedical researchers, and engineers who are involved in the fields of AI, bioinformatics, and precision medicine
1. Introduction to Multimodal Data in Healthcare: Opportunities and Challenges
2. Foundations of Multimodal Data Fusion: Techniques and Frameworks
3. AI-Driven Multimodal Diagnostics: Revolutionizing Patient Assessment
4. Fusion of Imaging and Genomic Data: Precision Medicine in Oncology
5. Integrating Clinical and Wearable Data: Real-Time Patient Monitoring
6. Deep Learning for Multimodal Data: Algorithms and Applications in Healthcare
7. Reinforcement Learning for Personalized Treatments Using Multimodal Data
8. Natural Language Processing and EHR Data: Enhancing Clinical Decision Support
9. AI Models for Multimodal Brain Imaging: Advances in Neurological Diagnostics
10. Multimodal Fusion for Cardiovascular Disease Prediction and Monitoring
11. Fusion of Molecular and Histopathological Data: AI in Pathology
12. Challenges in Data Integration: Addressing Bias, Privacy, and Interoperability
13. Edge Computing and IoT for Multimodal Health Data Processing
14. Ethics, Regulation, and Future Directions in AI-Driven Multimodal Healthcare
15. Case Studies: Successful Applications of Multimodal Data Fusion in Clinical Practice
2. Foundations of Multimodal Data Fusion: Techniques and Frameworks
3. AI-Driven Multimodal Diagnostics: Revolutionizing Patient Assessment
4. Fusion of Imaging and Genomic Data: Precision Medicine in Oncology
5. Integrating Clinical and Wearable Data: Real-Time Patient Monitoring
6. Deep Learning for Multimodal Data: Algorithms and Applications in Healthcare
7. Reinforcement Learning for Personalized Treatments Using Multimodal Data
8. Natural Language Processing and EHR Data: Enhancing Clinical Decision Support
9. AI Models for Multimodal Brain Imaging: Advances in Neurological Diagnostics
10. Multimodal Fusion for Cardiovascular Disease Prediction and Monitoring
11. Fusion of Molecular and Histopathological Data: AI in Pathology
12. Challenges in Data Integration: Addressing Bias, Privacy, and Interoperability
13. Edge Computing and IoT for Multimodal Health Data Processing
14. Ethics, Regulation, and Future Directions in AI-Driven Multimodal Healthcare
15. Case Studies: Successful Applications of Multimodal Data Fusion in Clinical Practice
- Edition: 1
- Latest edition
- Published: March 15, 2026
- Language: English
AS
Akansha Singh
Prof. Akansha Singh, Professor at the School of Computer Science and Engineering, Bennett University, Greater Noida, boasts a comprehensive academic background with a B.Tech, M.Tech, and Ph.D. in Computer Science. Her doctoral studies, conducted at the prestigious IIT Roorkee, were focused on the cutting-edge fields of image processing and machine learning. A prolific author and scholar, Dr. Singh has contributed over 100 research papers and penned more than 25 books. Her editorial expertise is recognized by leading publishers such as Elsevier, Taylor and Francis, and Wiley, where she has edited books on a variety of emerging topics.Dr. Singh serves as the Associate Editor in IEEE Access, Discover Applied Science, PLOS One and guest editor in several journals. Her research interests are diverse and influential, spanning image processing, remote sensing, the Internet of Things (IoT), Blockchain and machine learning. Prof. Singh’s work in these areas not only advances the field of computer science but also significantly contributes to the broader scientific and technological community.
Affiliations and expertise
School of CSET, Bennett University, Greater Noida, IndiaAD
Anuradha Dhull
Dr. Anuradha Dhull has more than 13 years of teaching experience at postgraduate and undergraduate level. She is a committed researcher in the field of data science and machine learning. She completed her graduation and post-graduation with distinction. Presently four doctoral scholars are pursuing their PhD under her supervision. She specializes in DBMS, Data Structures, Big data, Operating Systems and Analysis & Design of Algorithms. She has undertaken various academic responsibilities at the department and university level. She has also guided various M.Tech and B.Tech Projects. She has more than 35 research publications in peer-reviewed journals and is also serving as a reviewer for many journals.
Affiliations and expertise
The Northcap University, IndiaML
Monika Lamba
Dr. Lamba is an Assistant Professor in the Department of Computer Science and Engineering at The NorthCap University, Gurugram. She holds a B.Tech from Ansal Institute of Technology and an M.Tech from the University School of Information, Communication and Technology, both in Computer Science and Engineering. Dr. Lamba earned her Ph.D. in Machine Learning from The NorthCap University.
She has qualified for the GATE examination four times and has over seven years of experience in teaching and research. Dr. Lamba has presented papers at international conferences and published in SCI, ESCI, and Scopus-indexed journals. She has authored five book chapters, serves as a reviewer for several journals, and holds two patents. Additionally, she is a professional member of ACM and is certified in Microsoft AI-900 and AWS Cloud Practitioner.
Affiliations and expertise
The Northcap University, IndiaKS
Krishna Kant Singh
Dr. Krishna Kant Singh, currently the esteemed Director of Delhi Technical Campus in Greater Noida, India, is a highly experienced educator and researcher in the field of engineering and technology. He is a B.Tech and M.Tech degree, a Postgraduate Diploma in Machine Learning and Artificial Intelligence from IIIT Bangalore, a Master of Science in Machine Learning and Artificial Intelligence from Liverpool John Moores University, United Kingdom, and a Ph.D. from IIT Roorkee. Dr. Singh has made significant contributions to the academic and research community. With over 19 years of teaching experience, he has played a vital role in educating and mentoring future professionals. Dr. Singh also serves as an Associate Editor at IEEE Access, an Editorial Board Member at Applied Computing and Geosciences (Elsevier), and a Guest Editor for Complex and Intelligent Systems. His extensive publication record includes over 132 research papers. His areas of interest include Machine Learning, Deep Learning, computer vision and so on.
Affiliations and expertise
Delhi Technical Campus, Greater Noida, India