Table Of ContentArtifi cial
Intelligence in
Covid-19
Niklas Lidströmer
Yonina C. Eldar
Editors
123
Artificial Intelligence in Covid-19
Niklas Lidströmer • Yonina C. Eldar
Editors
Artificial Intelligence
in Covid-19
Editors
Niklas Lidströmer Yonina C. Eldar
Karolinska Institutet Weizmann Institute of Science
Stockholm, Sweden Rehovot, Israel
ISBN 978-3-031-08505-5 ISBN 978-3-031-08506-2 (eBook)
https://doi.org/10.1007/978-3-031-08506-2
© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature
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Foreword
We were surprised by the COVID-19 pandemic and ill-prepared for its many chal-
lenges. Given that we live in the age of artificial intelligence, we should not have
been. Artificial intelligence (AI) has emerged as the dominant technology of the
past decade, and advances in machine learning, in particular, have led to remarkable
innovations, ranging from automated speech recognition systems to self-driving
cars. However, prior to the pandemic, the impact of AI on infectious diseases and
public health had largely been anaemic, and as a result we were caught flat-footed
when the first COVID cases were reported in late 2019.
But as brilliantly captured by this timely book, the AI community quickly stepped
up to tackle many of the clinical and epidemiological challenges presented by the
pandemic. As you will read in these pages, AI has been harnessed in creative and
diverse ways to detect and treat COVID-19, as well as to develop predictive models
to identify emerging threats. Tremendous advances have been achieved throughout
the pandemic, but much work remains. As highlighted by this book, these outstand-
ing and future challenges will best be tackled by multi-lingual scientific and medi-
cal teams that thoughtfully integrate human intelligence with machine intelligence.
We all need hope in the face of challenges. This book offers us scientifically
grounded hope, both for the current pandemic and the next one. The tools of AI
provide us with a much-needed, unfair advantage in the battle of human wits versus
the genes of emerging pathogens. Thanks to AI, we will be better prepared for the
next pandemic.
MIT and Harvard James J. Collins
Cambridge, MA, USA
v
Preface
In 2020, earth was struck by the fifth deadliest pandemic in recorded history. It hap-
pened at the same time as modern Artificial Intelligence (AI) systems have been
impacting a wide range of areas from self-driving cars to automatic speech and
language recognition and healthcare.
AI has the potential to profoundly change all areas of healthcare from applica-
tions that can understand and classify clinical documentation, through automating
various tasks to analysing big patient data sets, and development of algorithms for
spotting tumours and other diseases, and guiding researchers in how to construct
cohorts for clinical trials.
AI has pivotal importance in the way we fight the present and future pandemics.
Here we present a plethora of areas where AI has been applied against COVID-19
and suggest areas in which AI can develop in order to better address future
pandemics.
This book is a practical, scientific, and clinically relevant example of how clini-
cians and researchers in medicine, computer science, mathematics, biology, and
epidemiology can fight future disasters on multiple fronts, and how scientific inte-
gration is crucial for progress.
The book aims at fellow researchers in areas ranging from medicine and biology
to computer science and mathematics, medical specialists and clinicians, medical
and pharmaceutical residents, and students, or interested academics in general, who
wish to know about the latest usage of AI in this medical field. Equipped with this
book the reader will learn about the latest AI advances against COVID-19, and how
mathematics and algorithms can aid in preventing its spreading course, treatments,
diagnostics, vaccines, clinical management, and future evolution.
We believe the book is needed both directly because of the seriousness of the
pandemic per se and because of the spread of misinformation. It is important that
the public and global organisations recognise more explicitly the role of science, for
it is with evidence-based science we can solve this incumbent and future threats to
our world and humanity.
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viii Preface
We wish to acknowledge and express our deep appreciation to all contributing
authors of this book, the Springer Nature publishing team, and our supporting
families who bore the pandemic with us.
Stockholm, Sweden Niklas Lidströmer
Rehovot, Israel Yonina C. Eldar
4 August 2022
Contents
1 Introduction to Artificial Intelligence in COVID-19 . . . . . . . . . . . . . . 1
Niklas Lidströmer and Yonina C. Eldar
2 AI for Pooled Testing of COVID-19 Samples . . . . . . . . . . . . . . . . . . . . 27
Ajit Rajwade, Nir Shlezinger, and Yonina C. Eldar
3 AI for Drug Repurposing in the Pandemic Response . . . . . . . . . . . . . 59
Anh T. L. Truong, Agata Blasiak, Mathias Egermark, and Dean Ho
4 AI and Point of Care Image Analysis for COVID-19 . . . . . . . . . . . . . . 85
Michael Roberts, Oz Frank, Shai Bagon, Yonina C. Eldar,
and Carola-Bibiane Schönlieb
5 Machine Learning and Laboratory Values in the Diagnosis,
Prognosis and Vaccination Strategy of COVID-19 . . . . . . . . . . . . . . . 121
Anna Carobene, Lorenzo Famiglini, Eleonora Sabetta,
Assunta Naclerio, and Giuseppe Banfi
6 AI and the Infectious Medicine of COVID-19 . . . . . . . . . . . . . . . . . . . 157
Vardan Andriasyan, Anthony Petkidis, and Artur Yakimovich
7 AI and ICU Monitoring on the Wake of the COVID-19 Pandemic . . . 169
Alfredo Vellido and Vicent Ribas
8 Symptom Based Models of COVID-19 Infection Using AI . . . . . . . . . 175
Songqiao Liu, Yuan Hong Sun, Alex A. Waese-Perlman, Nathan Yee
Lee, Haibo Zhang, and Kang Lee
9 AI Techniques for Forecasting Epidemic Dynamics:
Theory and Practice . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 193
Aniruddha Adiga, Bryan Lewis, Simon Levin, Madhav V. Marathe,
H. Vincent Poor, S. S. Ravi, Daniel J. Rosenkrantz,
Richard E. Stearns, Srinivasan Venkatramanan, Anil Vullikanti,
and Lijing Wang
ix
x Contents
10 Regulatory Aspects on AI and Pharmacovigilance for COVID-19 . . . 229
Vincenzo Salvatore
11 AI and the Clinical Immunology/Immunoinformatics
for COVID-19 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 239
Zikun Yang, Xiongye Xiao, and Paul Bogdan
12 AI and Dynamic Prediction of Deterioration in Covid-19 . . . . . . . . . 257
Carolin Danker, Marat Murzabekov, David Forsberg,
Niklas Lidströmer, Antoine Honoré, Susanne Rautiainen,
and Eric Herlenius
13 AI, Epidemiology and Public Health in the Covid Pandemic . . . . . . . 279
Thomas Lefèvre, Hélène Colineaux, Claire Morgand,
Laurent Tournois, and Cyrille Delpierre
Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 339
About the Editors
Niklas Lidströmer Karolinska Institutet, MD, MSc,
specialist physician, postgraduate researcher in AI in
medicine, senior advisor in AI and medical invest-
ments, former AI entrepreneur and founder of an
AI-powered medical platform, former head of Medical
AI at a variety of med-tech companies, and also previ-
ous co-leader of a handful of successful medical
start-ups.
His experience also encompasses widespread global
clinical work spanning 20 years within numerous
regions across eight countries. After graduating with a master’s thesis on global
medicine in 2000, he began practicing as a medical doctor in 2002, followed by
internship, specialised residencies, and clinical work all over the world, including
1 year circumnavigating as a maritime doctor.
His international work experience, fluency in nearly ten languages, practical
familiarisation with AI in the medical and pharmaceutical industries, and clinical
specialist competence in general medicine have produced a passion for translational
and educational aspects of artificial intelligence in medicine.
Dr. Niklas Lidströmer edited the pivotal reference work—the new standard refer-
ence, Artificial Intelligence in Medicine, Springer Nature (130 chapters, 1858
pages), which has now become the largest and most comprehensive in the scientific
community. The two volumes of this work are visible in the portrait image.
Dr. Niklas Lidströmer is presently the Chief Medical Officer for the cardiologi-
cal AI company Acorai (acorai.com), which has developed the new standard in
heart failure management. Its device with five sensors wirelessly gives clinicians
several intracardiac pressures, e.g., the mean pulmonary artery pressure, without the
operative catheterization.
Homepage at Karolinska Institutet: https://staff.ki.se/people/niklas- lidstromer
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