Artificial Intelligence and the Future of Public and Global Health: Promises, Expectations and Reality

Dear Colleagues,

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.

Dr. Arnold Kamis
Dr. Radmila Juric
Prof. Dr. Sang C. Suh
Guest Editors

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