Multimodal Temporal Modeling for Clinical AI: Data, Method, and Deployment

Cai, Jinjin; Wang, Ruiqi; Meng, Lingzhong; Su, Jing; Yang, Baijian

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.

Details

Published by: Springer

Publication Date: 2026-07-22

Format: Hardcover

ISBN-13: 9783032291387

DOI:

Dimensions: 235cm x155cm

Pages: 305