
Multimodal AI in Healthcare
A Paradigm Shift in Health Intelligence
Springer (Publisher)
Published on 29. November 2022
Book
Hardback
XXII, 416 pages
978-3-031-14770-8 (ISBN)
Description
This book aims to highlight the latest achievements in the use of AI and multimodal artificial intelligence in biomedicine and healthcare. Multimodal AI is a relatively new concept in AI, in which different types of data (e.g. text, image, video, audio, and numerical data) are collected, integrated, and processed through a series of intelligence processing algorithms to improve performance. The edited volume contains selected papers presented at the 2022 Health Intelligence workshop and the associated Data Hackathon/Challenge, co-located with the Thirty-Sixth Association for the Advancement of Artificial Intelligence (AAAI) conference, and presents an overview of the issues, challenges, and potentials in the field, along with new research results. This book provides information for researchers, students, industry professionals, clinicians, and public health agencies interested in the applications of AI and Multimodal AI in public health and medicine.
More details
Series
Edition
2023 ed.
Language
English
Place of publication
Cham
Switzerland
Publishing group
Springer International Publishing
Target group
Professional and scholarly
Illustrations
10 s/w Abbildungen, 91 farbige Abbildungen
XXII, 416 p. 101 illus., 91 illus. in color.
Dimensions
Height: 241 mm
Width: 160 mm
Thickness: 30 mm
Weight
822 gr
ISBN-13
978-3-031-14770-8 (9783031147708)
DOI
10.1007/978-3-031-14771-5
Schweitzer Classification
Other editions
Additional editions

Arash Shaban-Nejad | Martin Michalowski | Simone Bianco
Multimodal AI in Healthcare
A Paradigm Shift in Health Intelligence
Book
11/2023
Springer
€213.99
Shipment within 15-20 days

Arash Shaban-Nejad | Martin Michalowski | Simone Bianco
Multimodal AI in Healthcare
A Paradigm Shift in Health Intelligence
E-Book
11/2022
Springer
€213.99
Available for download
Content
Unsupervised Numerical Reasoning to Extract Phenotypes from Clinical Text by Leveraging External Knowledge.- Customized Training of Pretrained Language Models to Detect Post Intents in Online Health Support Groups.- EXPECT-NLP: An Integrated Pipeline and User Interface for Exploring Patient Preferences Directly from Patient-Generated Text.