Multimodal Temporal Modeling for Clinical AI

Data, Method, and Deployment
Erscheinungsjahr 2026
ISBN: 978-3-032-29138-7
Verlag: Springer Nature Switzerland AG

Buch, Englisch, 305 Seiten, Format (B × H): 155 mm x 235 mm

This book discusses how multimodal temporal AI is transforming healthcare by combining diverse medical data and health records over time. With clear explanations, cutting-edge methods, and real-world case studies, the book provides researchers, clinicians, and innovators the tools they need to turn AI breakthroughs into smarter and personalized care and treatment. Unlike existing literature that focuses narrowly on specific techniques or applications, this book provides a comprehensive, big-picture perspective on multimodal temporal modeling in clinical AI. The authors not only explain technical methods, but also explore the core principles, challenges, and future directions that shape the field. Readers will find practical guidance for deploying these models in real healthcare settings, along with actionable strategies that can be applied immediately. Covering the full spectrum of topics, from data to methods to deployment, the book offers a complete roadmap rather than fragmented insights. As a timely and up-to-date resource, the book captures the momentum of a rapidly evolving field and provides readers a forward-looking guide to the future of AI in healthcare.

Zielgruppe

Professional/practitioner

Autoren/Hrsg.

Cai, Jinjin

Wang, Ruiqi

Meng, Lingzhong

Su, Jing

Yang, Baijian

Fachgebiete

• Mathematik | Informatik EDV | Informatik Technische Informatik Computersicherheit
• Medizin | Veterinärmedizin Medizin | Public Health | Pharmazie | Zahnmedizin Medizin, Gesundheitswesen Medizinische Ethik
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz
• Mathematik | Informatik EDV | Informatik Computerkommunikation & -vernetzung Netzwerksicherheit
• Medizin | Veterinärmedizin Medizin | Public Health | Pharmazie | Zahnmedizin Pflege Krankenpflege
• Mathematik | Informatik EDV | Informatik Daten / Datenbanken

Inhaltsverzeichnis

Challenges in Collecting and Preparing Medical Data.- Standardization and Normalization of Medical Data.- Addressing Data Quality and Interoperability Issues.- Overview of State-of-the-Art AI Techniques.- Temporal Modeling Architectures in Clinical Data.- Fusion Strategies for Multimodal Clinical Data.- Joint Architectures for Fusion and Temporal Modeling in Healthcare.- Overcoming Technical Challenges in Deployment.- Clinical Validation and Regulatory Compliance.- Case Studies and Real-World Applications.- Conclusion: Future Directions and Opportunities.

Über Autor(innen)

Jinjin Cai is a Ph.D. candidate in the School of Applied and Creative Computing at Purdue University. Her research focuses on developing data-driven, human-centric AI methodologies for healthcare, with particular emphasis on representation learning and disease trajectory modeling to elucidate complex pathological processes. By integrating and extracting meaningful features from diverse multimodal medical data, she aims to enable earlier disease detection, enhance patient monitoring, and tailor interventions to individual needs. Her broader objective is to advance efficient, interpretable, and practical AI solutions that can inform clinical decision-making, streamline healthcare delivery, and improve patient outcomes.

Ruiqi Wang is a Ph.D. candidate in the School of Applied and Creative Computing at Purdue University. His research interests include multimodal perception and reasoning, human-centered adaptive systems, and human-in-the-loop robot learning. His work has been disseminated through leading journals and conferences in AI and robotics. His long-term goal is to enable the seamless integration of human- centered AI systems into everyday human life.

Lingzhong “LZ” Meng, M.D., is Professor of Clinical Anesthesia and Vice Chair for Clinical and Outcomes Research at Indiana University School of Medicine, with more than 20 years of experience across leading academic medical centers including Yale, UCSF, Duke, and Mayo Clinic. He is internationally recognized for pioneering research in perioperative cerebral blood flow, tissue perfusion monitoring, and integrating artificial intelligence into clinical decision-making—developing AI-enabled frameworks like HM- TARGET and DynaCEL for personalized hemodynamic management in critical care. An accomplished scholar with over 150 peer-reviewed publications, he has authored groundbreaking work in journals such as , and . As a committed educator and mentor, Dr. Meng founded the Global Perioperative Case Discussion Forum and has trained over 100 students, residents, and junior faculty worldwide.

Jing Su, Ph.D, is an Associate Professor in the Department of Biostatistics and Health Data Science at Indiana University School of Medicine. Dr. Su earned his Ph.D. in Biomedical Engineering from the Georgia Institute of Technology and Emory University. His work focuses on biomedical informatics, graph-based machine learning, and data management and the integration of longitudinal complex real- world clinical data. His research advances precision medicine through the development of temporal and multimodal analytical frameworks such as digital twin models and the implementation of graph models in real- world clinical care.

Baijian Yang, Ph.D., is the Associate Dean for Research at Purdue Polytechnic Institute and a Professor in the School of Applied and Creative Computing at Purdue University. With a Ph.D. in Computer Science from Michigan State University and degrees in Automation from Tsinghua University, he has authored over 100 peer-reviewed publications and two books on Smartphone programming. His research spans applied machine learning, big data analytics, cybersecurity, and notably, healthcare AI, where his work on multimodal, time-aware modeling (e.g., spatial transcriptomics methods like SpaRx) exemplifies the cutting edge of clinical AI. Dr. Yang also brings industry edge with CISSP, MCSE, and Six Sigma Black Belt certifications, bridging rigorous engineering with health-focused innovation.