The Future of Artificial Intelligence in International Healthcare: An Index

Proceedings of the Research Association for Interdisciplinary Studies Conference Proceedings of the 17th Interdisciplinary RAIS conference at Johns Hopkins University School of Medicine, Baltimore, Maryland, United States, June 1-2, 2020. [Held online due to COVID-19]

19 Pages Posted: 10 Jun 2020

See all articles by Julia M. Puaschunder

Julia M. Puaschunder

Harvard University; New School for Social Research; Columbia University; Princeton University; George Washington University Center for International Business Education and Research; The New School - Bernard Schwartz Center for Economic Policy Analysis (CEPA)

Date Written: June 9, 2020

Abstract

The currently ongoing COVID-19 crisis has challenged healthcare around the world. The call for global solutions in international healthcare pandemic outbreak monitoring and crisis risk management has reached unprecedented momentum. Digitalization, Artificial Intelligence (AI) and big data-derived inferences are supporting human decision making as never before in the history of medicine. In today’s healthcare sector and medical profession, AI, algorithms, robotics and big data are used as essential healthcare enhancements. These new technologies allow monitoring of large-scale medical trends and measuring individual risks based on big data-driven estimations. This article provides a snapshot of the current state-of-the-art of AI, algorithms, big data-derived inferences and robotics in healthcare. Examining medical responses to COVID-19 on a global scale makes international differences in the approaches to combat global pandemics with technological solutions apparent. Empirically, the article answers what countries have favourable conditions to provide AI-driven global healthcare solutions. First, an index based on internet connectivity – as a proxy for digitalization and AI advancement – as well as Gross Domestic Product (GDP) – as indicator for economic productivity – is calculated to outline global healthcare innovation hubs with economic impetus around the world. The parts of the world that feature internet connectivity and high GDP are likely to lead on AI-driven big data insights for pandemic prevention. When comparing countries worldwide, AI advancement is found to be positively correlated with anti-corruption. AI thus springs from non-corrupt territories of the world. Second, a novel anti-corruption artificial healthcare index is therefore presented that highlights those countries in the world that have vital AI growth in a non-corrupt environment. These non-corrupt AI centres hold comparative advantages to lead on global artificial healthcare solutions against COVID-19 and serve as pandemic crisis and risk management innovators of the future. Anti-corruption is also positively related with better general healthcare. Therefore, finally, a third index that combines internet connectivity, anti-corruption as well as healthcare access and quality is presented. The countries that score high on AI, anti-corruption and healthcare excellence are promoted as ultimate innovative global pandemic alleviation leaders. The advantages but also potential shortfalls and ethical boundaries in the novel use of monitoring Apps, big data inferences and telemedicine to prevent pandemics are discussed.

Keywords: Access to Healthcare, Advancements, AI-GDP Index, Apps, Artificial Intelligence (AI), Coronavirus, Corruption-Free Maximization of Excellence and Precision, Corruption Perception (CPI)-Global Connectivity (GCI) Index, Corruption Perception (CPI)-Global Connectivity (GCI)-Healthcare Index, COVID-19

Suggested Citation

Puaschunder, Julia M., The Future of Artificial Intelligence in International Healthcare: An Index (June 9, 2020). Proceedings of the Research Association for Interdisciplinary Studies Conference Proceedings of the 17th Interdisciplinary RAIS conference at Johns Hopkins University School of Medicine, Baltimore, Maryland, United States, June 1-2, 2020. [Held online due to COVID-19], Available at SSRN: https://ssrn.com/abstract=3623530 or http://dx.doi.org/10.2139/ssrn.3623530

Julia M. Puaschunder (Contact Author)

Harvard University ( email )

24 Oxford Street
Cambridge, MA 02138
United States

New School for Social Research ( email )

6 East 16th Street
New York, NY 10003
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Columbia University ( email )

3022 Broadway
New York, NY 10027
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Princeton University ( email )

22 Chambers Street
Princeton, NJ 08544-0708
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George Washington University Center for International Business Education and Research ( email )

2023 G Street NW
Washington, DC 20052
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The New School - Bernard Schwartz Center for Economic Policy Analysis (CEPA) ( email )

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5th Floor
New York, NY 10027
United States

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