CC BY-NC-ND 4.0 · Yearb Med Inform 2019; 28(01): 224-231
DOI: 10.1055/s-0039-1677910
Section 11: Public Health and Epidemiology Informatics
Survey
Georg Thieme Verlag KG Stuttgart

Public Health and Epidemiology Informatics: Can Artificial Intelligence Help Future Global Challenges? An Overview of Antimicrobial Resistance and Impact of Climate Change in Disease Epidemiology

Alejandro Rodríguez-González
1  Centro de Tecnología Biomédica, Universidad Politécnica de Madrid, Madrid, Spain
2  Escuela Técnica Superior de Ingenieros Informáticos, Universidad Politécnica de Madrid, Madrid, Spain
,
Massimiliano Zanin
1  Centro de Tecnología Biomédica, Universidad Politécnica de Madrid, Madrid, Spain
,
Ernestina Menasalvas-Ruiz
1  Centro de Tecnología Biomédica, Universidad Politécnica de Madrid, Madrid, Spain
2  Escuela Técnica Superior de Ingenieros Informáticos, Universidad Politécnica de Madrid, Madrid, Spain
› Author Affiliations
Further Information

Publication History

Publication Date:
16 August 2019 (online)

  

Summary

Objectives: To provide an oveiview of the current application of artificial intelligence (AI) in the field of public health and epidemiology, with a special focus on antimicrobial resistance and the impact of climate change in disease epidemiology. Both topics are of vital importance and were included in the “Ten threats to global health in 2019“ report published by the World Health Organization.

Methods: We analysed publications that appeared in the last two years, between January 2017 and October 2018. Papers were searched using Google Scholar with the following keywords: public health, epidemiology, machine learning, data analytics, artificial intelligence, disease surveillance, climate change, antimicrobial resistance, and combinations thereof. Selected articles were organised by theme.

Results: In spite of a large interest in AI generated both within and outside the scientific community, and of the many opinions pointing towards the importance of a better use of data in public health, few papers have been published on the selected topics in the last two years. We identify several potential reasons, including the complexity of the integration of heterogeneous data, and the lack of sound and unbiased validation procedures.

Conclusions: As there is a better comprehension of AI and more funding available, artificial intelligence will become not only the centre of attention in informatics, but more importantly the source of innovative solutions for public health.