-------- Forwarded Message --------
AI-driven health interventions and delivery have already started
affecting
the management of health services—from clinical decision making
and
predictions of mortality risk and healthcare planning to
identifying
disease outbreaks—thus addressing challenges in Public and Global
Health.
In this Special Issue, we would like to welcome papers which
discuss the
problems of suitability, reliability, and expectations of AI,
learning, and
predictive technologies in the Public/Global Health domains. This
includes
debates on the initiative for creating explainable and accountable
AI to
the public and to public health organizations. We would also like
to
receive papers which focus on the accuracy of the prediction
apparatus used
for forecasting in the time of pandemics, which may reveal their
various
perceptions, including public healthcare management and behavioral
issues
of individuals as well as societies. The three main themes of the
Special
Issue are itemized below. However, we also welcome any other
research in
progress and experiences of using learning and predictive
technologies when
the management of Public Health depends on our own manipulation of
data and
information generated during the COVID-19 pandemic.
We welcome papers in three areas of Artificial Intelligence
intersecting
with Public/Global Health.
1. Learning and predictive technologies facilitating Public Health
sectors’
decision making: trends, feasibility studies, practicality,
trustworthiness, and expectations:
- Predictive and learning technologies in the context of managing
PHO
domain knowledge including the impact of individual behavior and
group
dynamics to organizational impacts and government policies;
- Text and data mining, text sentiment analysis, and public
opinions for
the Public Health sector and their impact on decision making;
- Collaboration between PHO professionals and computer scientists:
from
creating joint, sustainable, reliable, and accountable AI
algorithms to
dealing with uncertainty, validation of models, interaction
effects, and
combinations (ensembles) of modeling principles;
- PHO practices based on case-based, history-based, rule-based, or
regression-based reasoning versus predictive and logic based
computational
inference typical of ML and AI algorithms;
- Transparency of AI and predictive models/algorithms to PHO
professionals and policymakers: searching for technically rigorous
and
accurate models and opaque black-boxes, or looking at the levels
of their
explainabilities, interpretability, and intuitiveness for PHO;
- Detecting paradigm shift in PHO regarding the deployment of AI:
searching for new models and expectations from AI promises. Public
Health
practitioners’/policymakers’ approaches to dealing with black-box
model
values in layman’s terms;
- AI accountability to public, PHO practitioners, policy makers
and
governments: awareness of bringing new software technologies,
computational
methods, and data processing to address issues and problems of PHO
and
their decision making;
2. The accuracy of traditional statistic forecasting in time of
pandemics
and impact of data/software tools on their results:
- Statistical and epidemiological models for predicting the
outcome of
epidemic/pandemic: characteristics, features, and parametrization
(the role
of R and its impact on modeling);
- Impact of human behavior, healthcare organization functions, and
efficient governance on statistical models in times of
epidemics/pandemics;
- Addressing the potentially biased nature of statistical models:
provisions for uncertainty, validation, testing, and quality;
- Improving the accuracy of the statistical models: from deploying
software tools and collection/manipulation of abundance of data to
creating
new computational models and using predictive and learning
technologies
with AI algorithmic computing;
3. Statistical models versus computational models versus data
models: do
they affect each other and do we understand the impact of their
potential
inter-relationship on modeling and predictions in the times of
pandemics?
Perceptions of pandemics, from governance, political decisions,
and
healthcare management to cultural and behavioral issues of
individuals and
societies:
- Human perception in modeling, predicting, and communicating the
results of a pandemic; the impact of human behavior on the
modeling and
interpretation/communication of modeling results;
- Converting the results of modeling and predictions into clear
messages, best practices, and adaptable procedures when managing
and living
during the pandemic, applicable to various types of audience;
- Assessing risks in the pandemic: how citizens, healthcare
professionals, business leaders, government leaders, opinion
leaders, and
celebrities assess the risks, costs, and benefits of different
responses to
the management of the pandemic;
- Scientific evidence versus persuasion when managing pandemics
and
making decisions: understanding the difference between abstract
modeling/predictions and real life situations;
- Going a step further beyond a list of DOs and DON’Ts when
managing a
pandemic by creating accurate, precise, and actionable
perceptions; making
modeling intrinsically sensitive to different cultures, debating a
tradeoff
between “saving lives” and downturns in economies, sharing
information and
data across borders, and developing trust among different parties
and
political establishments;
- Debating pandemics as an (un)avoidable, treatable, preventable,
natural phenomenon and the source for learning from the
experiences of
others.
For more information, see
https://www.mdpi.com/journal/ijerph/special_issues/Promises_Expectations_Reality
Best Regards,
Arnold Kamis, Brandeis University
Radmila Juric, University of South Eastern Norway
Sang C. Suh, Texas A&M University-Commerce
*Guest Editors*
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