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AI-BD-HS 2020 : Artificial Intelligence and Big Data in Resources Poor Healthcare Systems (Springer Nature Book series)


When N/A
Where N/A
Abstract Registration Due Dec 15, 2019
Submission Deadline Apr 30, 2020
Notification Due Jul 2, 2020
Final Version Due Aug 30, 2020
Categories    data science   health informatics   artificial intelligence   big data

Call For Papers

Call for book chapters

Submission Links
1. easychair:
2. cfplist:

Book Title
Artificial Intelligence and Big Data in Resources Poor Healthcare Systems
Trends, Perspectives, and Application

Artificial Intelligence (AI) and Big Data (BD) are the most useful technologies that could powerfully improve health care service delivery, especially in resource-poor countries (Resources Poor Healthcare Systems). Big Data basically consists of big amount of data coming from diverse sources in different formats, while AI includes machine learning (ML) and deep learning (DL) and can be used for repetitive tasks like diseases diagnosis, diseases screening, diseases prevention, early diseases detection, medical decision making, medical treatment using AI-based tools, nursing care at the end of life, home health and medical care, remote artificial doctor, etc.

Many countries, for example, China, are investing in AI for Healthcare to overcome challenges such as healthcare workforce shortage.

Doctors are slowly being assisted by a software system in their daily duties, for example in decision making. Though this can face some limitations. Most AI-based prediction tasks are done using DL technics/technology. However, DL applications are limited in explanatory capacity. A trained DL system cannot explain the “how” of performed predictions – even when the prediction outcomes are correct. This kind of “black box problem” is challenging, especially in healthcare, where doctors don't want to make life-and-death decisions without a firm understanding of how the machine arrived at its recommendation (even if those recommendations have proven to be correct in the past). Since Data sciences have great potential, combining Data sciences (including Data analytics) with AI will be beneficial for Healthcare one on hand and other help to solve the black box issues generated by DL. Data science combines different algorithms, technologies, and systems and assists to extract the right information from well designed and collected data.

This book is looking to explore the concepts of AI (including machine learning, deep learning) and big data (including IoT and Smart City) in health care along with the recent research development with a focus on health care systems resources poor countries (Resources Poor Healthcare Systems) as well as in rural areas in developed countries. It would also include various real-time/offline applications and case studies in the field of engineering, computer science, IoT, Smart Cities with modern tools & technologies used in healthcare.

As a population grows and resources become scarcer, the efficient usage of these limited goods becomes more important. Smart cities are a key factor in the consumption of materials and resources. Built on and integrating with big data, the cities of the future are becoming a realization today. The integration of big data and interconnected technology along with the increasing population will lead to the necessary creation of smart cities. To continue providing people with safe, comfortable, and affordable places to live, cities must incorporate techniques and technologies to bring them into the future. We are looking forward to seeing the advances that will come to our cities soon.

The main objectives of this book are to analyze the chances of effectively using AI in healthcare, especially in resource-poor countries (Resources Poor Healthcare Systems). Furthermore, this book aims at presenting some use cases of AI in healthcare and though show its potential to improve care services delivery, overcome the forthcoming challenges, which will face the sector regarding the increasing world population, as well as resource scarcity and workforce shortage.

The reader will be provided with insight into AI, Big data, and Data sciences for healthcare, the state-of-the-art, chances, challenges, limitations, and perspectives, as well as future development. This book would cover disciplines such as, but not limited to, Artificial Intelligence and Big Data in Resources Poor Healthcare Systems: Theories, Chances, Trends, Perspectives, Future Development, and Applications is a fascinating addition to the literature in this discipline.

Expert system
Machine learning
Deep learning
Drugs discovery
Big data analytic
Healthcare technology
Explainable artificial intelligence

Unique features of the book

Explores the concepts of AI (including machine learning, deep learning) and big data (including the Internet of Things and Smart City) in healthcare.
Includes various real-time/ offline applications and case studies in the field of engineering, computer science, IoT, Smart Cities with modern tools & technologies used in healthcare.
It provides guidance on how health technology can face the challenge of improving the quality of life regardless of social and financial consideration, gender, age, and residence place.

Submission Guidelines
All papers must be original and not simultaneously submitted to another journal or conference. The following paper categories are welcome (but not limited to):

Technical Design Papers
Scientific Evaluation Papers/Systematic Review
Case reports
Vision Papers

Chapter proposal submission: December 15th, 2019 (max. 500 words)
Acceptance notification: December 20th, 2019
Full chapters submission: April 30th, 2020 (max. 5 tables/figures, 25 pages, 15.000 to 20.000 words)
Full chapter acceptance notification: July 2nd, 2020
Final and revised chapters due to: August 30th, 2020

Template and SpringerNature format
Please use the Springer Nature format. For detail click here.
List of Topics

Health informatics/Medical IT
Health care Services Delivery and management
Assistive Decision Making
Data science
Artificial intelligence

Tentative Chapters
Introduction to AI and Big data for resource-poor Healthcare
Introduction to Deep learning for resource-poor healthcare
Introduction to smart healthcare in the context of big data and AI
Challenges facing AI for Healthcare resource-poor Healthcare
AI and Big Data in Healthcare for resource-poor Healthcare: Trends and perspectives
Convolutional neural network for improving resource-poor Healthcare
Deep believe network for medical and health applications in resource-poor Healthcare
Deep learning network architectures for medical and health applications to improve resource-poor Healthcare.
Stack Autoencoder for multimodal health care to improve resource-poor Healthcare
Deep learning scalability models in improving resource-poor Healthcare
Expert system for improving resource-poor Healthcare
Expert systems in smart hospitals and clinics
Developing smart doctors based on explainable AI for resource-poor Healthcare
Explainable AI-based Remote Care to improve resource-poor Healthcare
Explainable AI-based Medical Data Exchanges and Data interoperability for resource-poor Healthcare
Explainable AI-based Electronic Health Records for resource-poor Healthcare
Improving resource-poor Healthcare based on Explainable AI
Expert system for exploring protein secondary structure to improve resource-poor Healthcare
Exploiting structure in information for resource-poor Healthcare
Making use of heterogeneous information in machine learning for resource-poor Healthcare.
Monitoring to personalized medicine through expert systems for resource-poor Healthcare.
Improving patient privacy and security through expert systems for resource-poor Healthcare
Deep learning in drug discovery for resource-poor Healthcare
Deep Transfer Learning in protein structure for resource-poor Healthcare
Hybrid of nature-inspired algorithm and deep learning structure for improving resource-poor Healthcare in developing countries
Case studies of AI and big data in Healthcare delivery in improving resource-poor Healthcare

Editorial Advisory Board Members
Samuel Fosso-Wamba, Toulouse Business School, France
Gunnar Teege, German Federal Army Munich, Germany
Haruna Chiroma, Federal College of Education, Gombe, Nigeria
Jules Degila, IMSP, Université d’Abomey-Calavi, Benin
Pravin Pawar, SUNY, Korea.
Vania V. Estrela, Universidade Federal Fluminense, RJ, Brazil and FEEC, UNICAMP. SP

Thierry Edoh, RFW-Universität Bonn, Bonn Germany
Vijayalakshmi Kakulapati, Sreenidhi Institute of Science and Technology, Yamnampet, Ghatkesar, Hyderabad India

This book will be published in Series Health Informatics (

by Springer Nature Switzerland AG. The address for which is: Gewerbestrasse 11, 6330 Cham, Switzerland Copyright Holder: Springer Nature Switzerland AG
All questions about submissions should be emailed to Thierry Edoh (

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