AI in Public Health: Revolutionizing Healthcare in Digital Era - Essay Sample

Introduction

Artificial intelligence is public health is generally the use of software and complicated algorithm to relate to the evaluation of medical information. It also has the ability to estimate conclusions without direct human input. As a rapidly revolutionizing field, artificial intelligence in public health has become a significant part of healthcare, especially in this digital era where technology information and its availability dictate the success or failure of any process. In public health, it is rapidly incorporated to enhance health services through the use of machines to compute or estimate heath conclusions without human input. One of the major advantages of artificial intelligence is the fact that, different from other technologies, it can gain information, process it, and come up with logical conclusions. Thus, over the recent years, researchers have detailed the importance of artificial intelligence in public health in the bid to bring out awareness on its relevance.

Is your time best spent reading someone else’s essay? Get a 100% original essay FROM A CERTIFIED WRITER!

Save 25%

Literature Review

Rodriguez-Gonzalez, Zanin, and Menasalvas-Ruiz (2019) looks into the contemporary use of artificial intelligence in epidemiology and public health. The article provides a profound look into the effect of climate change on disease epidemiology and antimicrobial resistance. The authors utilized qualitative research by analyzing publications in the period between January 2017 and October 2018. The articles were sampled by theme as they were searched on Google scholar by entering the following keywords: antimicrobial resistance, climate change, disease surveillance, artificial intelligence, data analytics, machine learning, epidemiology, and public health. The article is valid in the sense that it provides evidence-based information from previously published scholarly papers on the topic. The research is also reliable as it looks deeper into the topic and its related areas such as antimicrobial resistance, climate change, disease surveillance, artificial intelligence, data analytics, machine learning, epidemiology, and public health. Rodriguez-Gonzalez, Zanin, and Menasalvas-Ruiz (2019) recommends that with increased funding, AI can become the center of attention in informatics and a source of innovative solutions in public health.

In another research by Sincak Et al. (2014), the authors analyzed various strategies that can be used to incorporate hot water tank with artificial intelligence tools in the bid to prevent legionellosis. The primary purpose of the study is to present an experimental and theoretical evaluation of hot water systems in pursuit of reducing microbiological risks in Slovakia by employing some of the artificial intelligence principles. The research is highly valid as it provides evidence-based study with a mixed methodology of experiments and theoretical approaches. The study can have a great significance on the legislation, providers, and patients as it can be useful in measures to prevent the spread of legionella bacteria. The research recommends more research on AZURE cloud computing and general artificial intelligence implementation to establish a universal solution so that it can be employed in general environments.

Thiebaut and Thiessard (2018) generally summarize research on epidemiology informatics and public health. The research utilized qualitative research where literature concerning epidemiology informatics and public health in 2017 from PubMed and Web was analyzed by editors. The authors suggest that epidemiologic strategies begin with appropriate questions, careful selection of important information, and validation of the results. Additionally, evaluating social media and other types of applications is important in public health. However, artificial intelligence incorporates various methods such as statistical and machine learning, which its use is not limited to pairing them with the right questions. The article is valid as the research papers were searched through comprehensive bibliographic databases that targeted epidemiology and public health researches. Thiebaut and Thiessard (2018) recommend that digital materials such as social media can be a significant communication tool in public health. The authors also detail the importance of evaluating how new knowledge and interventions correspond in public health performance.

Lastly, in a study by Ward Et al. (2019) the authors feel that timely data is important in public health responses to epidemics. The purpose of the research is generally to create a machine learning approach to sample death certificates, which offer faster mortality surveillance on drug overdose. The research utilized a quantitative approach where they used the 2017-2018 free-text fields, Kentucky death certificates features were created using natural language processing. The author concluded that the use of natural language processing might be a new strategy, but it's effective. The research is valid as scholarly research from the University of Kentucky. The study recommends that this method is an accessible machine learning application that enhances the timeline of drug overdose mortality surveillance. Thus, it can be used in public health for faster analysis in case of drug overdose epidemics.

Conclusion

In conclusion, it is evident that all these researches provide significant information that can be used to improve public health. For instance, more research should be conducted on AZURE cloud computing and general artificial intelligence implementation to establish a universal solution so that it can be employed in general environments. The studies are also relatable in the modern world as most of the range in the previous five years.

References

Rodriguez-Gonzalez, A., Zanin, M., & Menasalvas-Ruiz, E. (2019). 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. Yearbook of medical informatics, 28(01), 224-231.

Sincak, P., Ondo, J., Kaposztasova, D., Vircikova, M., Vranayova, Z., & Sabol, J. (2014). Artificial intelligence in public health prevention of legionelosis in drinking water systems. International journal of environmental research and public health, 11(8), 8597-8611.

Thiebaut, R., & Thiessard, F. (2018). Artificial intelligence in public health and epidemiology. Yearbook of medical informatics, 27(01), 207-210.

Ward, P. J., Rock, P. J., Slavova, S., Young, A. M., Bunn, T. L., & Kavuluru, R. (2019). Enhancing timeliness of drug overdose mortality surveillance: A machine learning approach. PloS one, 14(10).