The global challenge against non-communicable diseases gained transnational attention over the last years. Statistics illustrate, that approximately 70% of all deaths worldwide can be traced back to chronic diseases. On the other hand, artificial intelligence is on the rise as the number of implemented software featuring machine-learning skills increased tremendously in the last years. Therefore, the purpose of this literature review is to combine those aspects in order to create synergies of the link between existing approaches with the aim to face non-communicable diseases and artificial intelligence. The review focusses firstly specifically only on the most prevalent NCDs and their characteristics before analysing the rise of machine-learning software from the very beginning. In a next step, those two fields will be combined with the aim to point out both benefits and risks when implementing AI in the healthcare sector. The outcome was, that the use of such software would definitely cut a three-digit number of billions of Euros of spending on healthcare due to the possibility to early-detect diseases and the hereby arising possibility to treat patients in a more efficient way as until now. On the other hand, the biggest risk will be, that artificial intelligence works on huge datasets that need to be set up first – during the setting up process of such data, failures can take place which would have a grave impact on further use.
Table of Contents
1. INTRODUCTION
2. NON-COMMUNICABLE DISEASES
2.1 GENERAL INFORMATION ABOUT THE MOST PREVALENT NCDS
2.2 HEALTH PROMOTION AND PREVENTION PROGRAMS
2.2.1 THE HIGH-RISK APPROACH
2.2.2 THE POPULATION APPROACH
3. ARTIFICIAL INTELLIGENCE
3.1 HISTORICAL DEVELOPMENT
4. ARTIFICIAL INTELLIGENCE IN THE HEALTHCARE SECTOR
4.1 BENEFITS
4.2 RISKS
Objectives and Research Themes
This literature review aims to explore the potential synergies between artificial intelligence (AI) and the management of non-communicable diseases (NCDs), analyzing how AI-driven technologies can improve health promotion, early detection, and treatment efficiencies within the healthcare sector.
- The global burden and prevalence of non-communicable diseases.
- Distinctions between health promotion and disease prevention programs.
- Historical evolution and definitions of artificial intelligence and machine learning.
- Benefits and risks of integrating AI into modern healthcare systems.
- Economic implications of early disease detection and treatment.
Excerpt from the Book
2.2.1 The High-Risk Approach
The high-risk approach (see: Figure 2) describes clinical, individual prevention. Its practices are counselling to change the individual risk behaviour, medication to delay the occurrence of the disease and screening to early detect an asymptomatic disease. Examples for a high-risk approach are: breast cancer screening, blood pressure checks, safe sex counselling, etc. Basically, the high-risk strategy can also be compared to a “face to face intervention”, as it controls the individual behaviour.
The positive aspects about this strategy are, that the intervention is appropriate for an individual at risk and that there is a favourable benefit-risk ratio. Furthermore, the motivation for an intervention like this is high for both, the physician and the subject (= individual).
Negatives, that should be outlined are, that the costs for high-risk interventions are extremely high most of the time (e.g. screening). Secondly, the potential for the whole population is limited.
Summary of Chapters
1. INTRODUCTION: This chapter provides an overview of the global prevalence of non-communicable diseases and highlights the need for modern technological interventions like artificial intelligence to address these health burdens.
2. NON-COMMUNICABLE DISEASES: This section defines NCDs, discusses their impact, and differentiates between various health promotion and prevention strategies, including high-risk and population-based approaches.
3. ARTIFICIAL INTELLIGENCE: This chapter introduces the concept of AI, its rapid integration into everyday life, and traces the historical development of machine learning from early logical theories to modern intelligent systems.
4. ARTIFICIAL INTELLIGENCE IN THE HEALTHCARE SECTOR: This final section examines the practical application of AI in healthcare, weighing significant benefits like cost reduction and improved diagnosis accuracy against risks such as data security and ethical concerns.
Keywords
Artificial Intelligence, Machine Learning, Non-communicable diseases, NCDs, Healthcare, Prevention, Health Promotion, High-risk approach, Population approach, Early detection, Digitalization, Expert systems, Chronic diseases, Disease prevention, Patient outcomes
Frequently Asked Questions
What is the primary focus of this literature review?
The review investigates the intersection of artificial intelligence and healthcare, specifically focusing on how AI can assist in the early detection and treatment of non-communicable diseases.
What are the central themes discussed in the work?
The core themes include the burden of chronic diseases, the efficacy of different public health prevention strategies, the history of AI development, and the pros and cons of implementing AI in medical environments.
What is the main research goal?
The goal is to determine whether leveraging modern resources like AI can decrease the probability of suffering from NCDs and improve the efficiency of patient treatment.
Which scientific methodology is applied?
The author conducts a structured literature review, analyzing health reports, clinical studies, and historical developments in AI to synthesize current knowledge on the topic.
What topics are covered in the main section?
The main section covers the definition and characteristics of NCDs, the difference between high-risk and population-based prevention, the evolution of intelligent machines, and specific benefits and risks of AI in the healthcare sector.
Which key terms characterize this research?
Key terms include Artificial Intelligence, NCDs, Machine Learning, Early Detection, and Healthcare Innovation.
What is the significance of the "prevention paradox" mentioned in the text?
It describes a situation where a preventative measure provides a large benefit to the population as a whole, but offers relatively little discernible benefit to each individual participant.
How does the author characterize an "expert system"?
An expert system is defined as software that utilizes expert knowledge to provide advice on solving specific, often non-structured problems.
What potential does AI hold for breast cancer diagnosis?
The research suggests that AI can analyze mammography results significantly faster than human doctors, with lower error rates and substantial potential for cost savings.
What are the primary concerns regarding AI implementation in healthcare?
The main risks identified are the necessity of high-quality data to avoid discrimination, the vulnerability of patient privacy to cybercrime, and the potential for AI-generated information to influence decision-making processes.
- Quote paper
- Julius Holaus (Author), 2019, Artificial intelligence in healthcare. An analysis of the link of AI to health promotion and prevention programs to face and early-detect non-communicable diseases, Munich, GRIN Verlag, https://www.hausarbeiten.de/document/491644
