E-health latin america and the caribbean progress and challenges
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Executive Summary
In today's technological environment, Artificial Intelligence (AI) is the most promising rising movement. Artificial intelligence (AI) is intelligence shown by computers, as opposed to natural (human or animal) intelligence. AI, also known as Machine Intelligence, is a type of artificial intellect that tries to replicate human intelligence by acquiring and applying information and skills. In the healthcare industry, artificial intelligence (AI) has been breaking new ground by aiding physicians, hospitals, pharmaceutical firms, and others in overcoming practical difficulties. The number of businesses focused on AI implementation in the healthcare industry has increased. A global lack of healthcare personnel is being felt by a rising population, and the gap is expected to increase over time. The COVID-19 pandemic's tremors have ushered in a new era of healthcare difficulties throughout the planet. Severe problems and challenges have been seen in Australia in areas such as patient consultations, remote monitoring, medical resources, and healthcare workers, among others. During the COVID-19 epidemic in the Australia, this study aims to provide a comprehensive picture of digital healthcare. It encompasses a variety of activities, such as mobile apps, web platforms, and sophisticated analytics, all aimed at early detection and total healthcare management. In addition to briefly outlining the major challenges that must be addressed in order for eHealth paradigms to be widely implemented. This research also focuses on several essential features of Artificial intelligence and the Internet of Things, as well as its prospective applications, such as clinical decision - making and predictive modeling, particularly in the context of tackling the COVID-19 pandemic's developing problems.
Table of Contents
List of Abbreviations & Acronyms
AI Artificial Intelligence
WHO World Health Organization
IoT Internet of Things
List of Illustrations
Figure 1: A timeline of the COVID-19 pandemic
List of Keywords
Introduction
Background
Artificial intelligence (AI) and deep learning technologies are changing the healthcare sector. Healthcare providers have gathered huge data sets in the form of medical information and pictures, demographic data, claims data, and clinical study data. AI technologies are perfectly suited to analyzing such data and uncovering trends and correlations that people could not discover through their own. Machine learning using AI allows the healthcare companies to utilize analytics to assist them make much better financial and clinical choices, as well as enhance the quality of the solutions they deliver. Between 2017 and 2023, spending on artificial intelligence in healthcare is expected to rise at a 48 percent yearly rate. Artificial intelligence, or AI, refers to the idea that robots may be trained to replicate human decision-making and learning characteristics. Artificial intelligence (AI) and robotic technologies have the potential to significantly improve the usage and implementation of telehealth throughout coronavirus disease (COVID-19) and the post-pandemic environment. Intelligence activities have the potential to transform how well the healthcare industry operates, from hospital care to clinical research, medication research, and insurance, in order to increase efficiency and enhance patient outcomes. According to the most current Australian statistics study, AI in healthcare services was valued at USD 2.1 billion in 2018 and is expected to be worth USD 36.1 billion by 2025. The most cutting-edge medical services firms have simply realized this and have included it into their method. Organizational development has resolved to make any type of impact in that sector. However, rivalry will be fierce as AI covers with a strong business - The Tech Industry. It is not a new idea; IT companies have been debating moving to social insurance for a long time; nevertheless, as we approach 2020, it will become increasingly clear. We will also see the increase of locally advanced time frame operations within few implementations may be a risk for the medicinal services framework, while it is not all doom and gloom as it is furthermore a fantastic source of potential valued establishment even as Technology Sector is trying to capitalize on undiscovered and unexpected opportunities. The global healthcare system has been devastated by the COVID-19 epidemic. While other industrial sectors have suffered as a result of the idleness induced by lockdowns and travel bans, the healthcare business is far from stagnant. Ventilators, intensive care units (ICUs), and personal protective equipment (PPE) are in limited supply in hospitals throughout the world as a result of the COVID-19 outbreak. Because of the rapidly rising number of COVID-19 patients, even the world's most affluent countries' healthcare systems are on the verge of collapse. This part is mostly concerned with the research purpose and scope, objectives, research goal, and research satisfaction. This research looks at the influence of artificial intelligence in Australian healthcare. Then, it creates benchmark techniques to assist healthcare providers in providing a more complete environment. This research also includes a thorough examination of critical success criteria in AI deployment in Australian healthcare (Karim & Sandu, 2020). The brief research is a call to integrate AI, robotics, and telemedicine with an AI-powered organizational framework to speed healthcare delivery and increase access to healthcare during epidemics or public health crises such as COVID-19.
Problem Statement
With the introduction of COVID-19, the whole medical system in several regions across the globe was put under great strain by increasingly infected individuals. The highly important regulations of freshwater, cleanliness, safety, and waste treatment were introduced to the strain, implying new problems in treating patients in hospitals and medical institutions. The Australian healthcare business faced a number of problems, the most significant of which were a scarcity of human resources in key healthcare services, the coordination of treatments for routine patients, and a rapid spike in demand for particular medical equipment. Moreover, in pandemics, these problems brought up new opportunities for IoT and AI in the health industry.
Research Questions
What is the AI application in the COVID-19 pandemic?
Will the healthcare industry's interest in artificial intelligence continue to grow?
Limitations
Scope
Significance
Annotated Bibliography
Abir, S., Islam, S., Anwar, A., Mahmood, A., & Oo, A. (2020). Building Resilience against COVID-19 Pandemic Using Artificial Intelligence, Machine Learning, and IoT: A Survey of Recent Progress. Iot, 1(2), 506-528. https://doi.org/10.3390/iot1020028
This article provides a thorough examination of the AI, machine learning, and Internet of Things (IoT) technologies that might be used to combat the COVID-19 pandemic. There includes a thorough examination of the enabling tools and methodologies, as well as present and prospective AI, ML, and IoT applications. There is also a critical examination of the hazards and limits of the aforementioned technology.
Elansary, I., Darwish, A., & Hassanien, A. (2021). The Future Scope of Internet of Things for Monitoring and Prediction of COVID-19 Patients. Digital Transformation And Emerging Technologies For Fighting COVID-19 Pandemic: Innovative Approaches, 235-247. https://doi.org/10.1007/978-3-030-63307-3_15
This article presented an overview of Internet of Things (IoT) technologies employed in the fight against the deadly COVID-19 epidemic in various applications, as well as a discussion of the critical roles of IoT research in this unprecedented conflict. The research paths on finding IoT's potentials, enhancing its capabilities and power in warfare, and IoT's challenges and problems in healthcare systems are all thoroughly studied. The goal of this study is to give IoT researchers and the larger community an overview of the present state of IoT applications, as well as to encourage them to use IoT to combat COVID-19.
Umair, M., Cheema, M., Cheema, O., Li, H., & Lu, H. (2021). Impact of COVID-19 on IoT Adoption in Healthcare, Smart Homes, Smart Buildings, Smart Cities, Transportation and Industrial IoT. Sensors, 21(11), 3838. https://doi.org/10.3390/s21113838
In this paper, COVID-19 is proven to be a technology adoption and development driver. We studied recent research literature, looked at reports from top consulting companies, and spoke with a variety of professionals from various fields. In addition, we identify a number of obstacles that must be solved as well as significant research paths that must be pursued in order to promote faster IoT adoption in various industries. The authors also give data on major IoT projects launched in the aftermath of COVID-19 for each of these industries. We also discuss the problems that must be solved, as well as significant research paths that will speed up IoT implementation.
Literature Review
Research Methodology
Data Collection Method
Journals,
Interview- sample size, mode of interview, types of question asked.
Data Analysis Method
Research Process
Analysis
In this section, we provide an overview of the use of artificial intelligence (AI) to combat coronavirus outbreaks. Outbreak estimate, coronavirus detection, coronavirus analytics, vaccine/drug development, and prediction of future coronavirus-like outbreaks are the applications that AI may help with.
Artificial intelligence (AI) systems have spotted an epidemic of an unknown kind of pneumonia in the People's Republic of China (hereinafter "China") before the world was even aware of the threat presented by the coronavirus (COVID-19). As the outbreak has spread globally, AI techniques and technology may be used to aid policymakers, the medical community, and society at large in managing every stage of the crisis and its aftermath, including detection, prevention, response, and recovery, as well as to speed up research.
Figure 4: Bluetooth-enabled smartphone apps for COVID-19 contact tracing.
Vaccine development with artificial intelligence
The recent appearance of coronavirus has brought attention to the need of forecasting future outbreaks like COVID-19. In the fight against the coronavirus, epidemic models of COVID should be considered to anticipate and control the outbreak. AI has recently been used to anticipate coronavirus outbreaks. For instance, an AI-based prediction model can estimate the magnitude, lengths, and end time of COVID-19 in China. Based on data gathered from WHO sources, an auto-encoder is being developed for modeling the epidemic's transmission patterns. Coronavirus epidemics have recently been predicted using AI. For example, an AI-based prediction model can forecast COVID-19's size, duration, and termination date in China. An auto-encoder for modeling the epidemic's transmission patterns is being created based on data obtained from WHO sources. It examines the characteristics, causes, and motivations for infection dissemination. In the future, this technology will be crucial in the battle against additional diseases and pandemics. It can be used as a preventative strategy as well as a treatment for a variety of different ailments. AI will become increasingly important in offering better predictive and preventative healthcare in the future.
Findings
According to the data measured it was found that 40% of the respondent believed that …… and the according to the author….. which clearly shows the efficacy of AL in health care.
Through patient needs to voice assistants, Computer aided systems for diagnostics, and imaging data analysis to identify drug candidates in drug research, AI already is improving effectiveness and speed, lowering costs and mistakes, and making it simpler for even more patients to obtain the treatment they require. While AI is currently being utilized in health care, it’s potential to (Deloitte, 2021):
Enhance the productivity and quality of care of providers and clinicians.
Recommendations
High-quality AI systems require a lot of data. The best AI advice is to start with real problems in health care, explore the best solutions by engaging relevant stakeholders, frontline users, patients and their families—including AI and non-AI options—and then implement and scale the ones that meet our Quintuple Aim: better health, improved care experience, clinician well-being, lower cost, and health equity across the board.
To outline the problem, discover relevant data and open datasets, provide tools, and train models, encourage multidisciplinary and multi-stakeholder collaboration and data sharing both domestically and globally by the AI community, medical community, developers, and policymakers.
Conclusion
References
Abir, S., Islam, S., Anwar, A., Mahmood, A., & Oo, A. (2020). Building Resilience against COVID-19 Pandemic Using Artificial Intelligence, Machine Learning, and IoT: A Survey of Recent Progress. Iot, 1(2), 506-528. https://doi.org/10.3390/iot1020028
Arora, N., Banerjee, A., & Narasu, M. (2020). The role of artificial intelligence in tackling COVID-19. Future Virology, 15(11), 717-724. https://doi.org/10.2217/fvl-2020-0130
Chamola, V., Hassija, V., Gupta, V., & Guizani, M. (2020). A Comprehensive Review of the COVID-19 Pandemic and the Role of IoT, Drones, AI, Blockchain, and 5G in Managing its Impact. IEEE Access, 8, 90225-90265. https://doi.org/10.1109/access.2020.2992341
Deloitte. (2021). Future of Artificial Intelligence in Health Care. Deloitte United States. Retrieved 7 August 2021, from https://www2.deloitte.com/us/en/pages/life-sciences-and-health-care/articles/future-of-artificial-intelligence-in-health-care.html.
Naudé, W. (2020). Artificial intelligence vs COVID-19: limitations, constraints and pitfalls. AI & SOCIETY, 35(3), 761-765. https://doi.org/10.1007/s00146-020-00978-0
Ndiaye, M., Oyewobi, S., Abu-Mahfouz, A., Hancke, G., Kurien, A., & Djouani, K. (2020). IoT in the Wake of COVID-19: A Survey on Contributions, Challenges and Evolution. IEEE Access, 8, 186821-186839. https://doi.org/10.1109/access.2020.3030090
Vaishya, R., Javaid, M., Khan, I., & Haleem, A. (2020). Artificial Intelligence (AI) applications for COVID-19 pandemic. Diabetes & Metabolic Syndrome: Clinical Research & Reviews, 14(4), 337-339. https://doi.org/10.1016/j.dsx.2020.04.012
