Multimodal AI in Healthcare: A Paradigm Shift in Health Intelligence
Author:   Arash Shaban-Nejad ,  Martin Michalowski ,  Simone Bianco
Publisher:   Springer International Publishing AG
Edition:   1st ed. 2023
Volume:   1060
ISBN:  

9783031147708


Pages:   416
Publication Date:   29 November 2022
Format:   Hardback
Availability:   Manufactured on demand   Availability explained
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Multimodal AI in Healthcare: A Paradigm Shift in Health Intelligence


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Overview

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.

Full Product Details

Author:   Arash Shaban-Nejad ,  Martin Michalowski ,  Simone Bianco
Publisher:   Springer International Publishing AG
Imprint:   Springer International Publishing AG
Edition:   1st ed. 2023
Volume:   1060
Weight:   0.822kg
ISBN:  

9783031147708


ISBN 10:   3031147707
Pages:   416
Publication Date:   29 November 2022
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Hardback
Publisher's Status:   Active
Availability:   Manufactured on demand   Availability explained
We will order this item for you from a manufactured on demand supplier.

Table of Contents

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.

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