This bachelor thesis is based on the approach of providing nowadays society with solutions through the advancement of Artificial Intelligence in order to improve their quality of life. For the reason that the obstacles within the health sector are evolving in a negative sense a special focus within this concept is laid on Artificial Intelligence systems impact within the health sector of mental illnesses. Thus being said the structure of the thesis focuses on four key theories in the beginning which arise over and over within the thesis. These four key theories included: The Wave Approach by Toeffler, the definition of three identities, Neuroscience and Mental Illnesses (burn-out, depression and anxiety). Nevertheless, the thesis was examined in several other parts such as Artificial Intelligence and the technology behind it, the implementation of AI in smart cities, pattern recognition and monitoring in the health sector.
By focusing on such areas, analysing and connecting them with past approaches the impact of the data was described and analysed. Thus being said the thesis approach was coming to the conclusion that through the implementation of Artificial Intelligence systems such as voice recognition systems and facial recognition systems the area of diagnosing mental illnesses and improving treatment as well as reaching for quicker response rate — in regards to emergencies — can be reached.
Table of Contents
1.0 Introduction
1.1 Background Information
1.2 Thesis Topic
1.3 Thesis & Hypotheses
2.0 Methodology
2.1 Literature Review
2.2 Case study
2.3 Expert Interviews
3.0 Key Theory Definitions & Explanations
3.1 The Wave Approach
3.2 Identities of the Society
3.3 Neuroscience
3.4 The Correlation between Depression, Anxiety and Burn-out
4.0 The Technology behind Artificial Intelligence
4.1 The Internet of Things
4.2 The Rise of Smart Cities and its Impact on Human Life
4.3 Smart Devices as AI & Data Collection
4.4 Challenges of AI in its implementation in Smart Cities
5.0 AI in Smart Cities with the Focus on Health
5.1 The Role of AI in Mental Health
6.0 Societal & Cultural Transformation
7.0 Expert Interview Analysis
8.0 Futuristic Outlook
9.0 Discussion and Conclusion
10.0 References
11.0 Appendix
11.1 Appendix A: Interview questions in German
11.2 Appendix B: Interview Questions in English
11.3 Appendix C: Interview 1 with Michael Dehm
11.4 Appendix D: Interview 2 with Nicoletta Blaschke
11.5 Appendix E: Interview 3 with Lothar Hotz
Objectives & Core Topics
This thesis investigates how Artificial Intelligence (AI) monitoring and smart technologies can improve the quality of life in modern society, with a specific focus on the health sector and mental illnesses such as depression, anxiety, and burn-out. The primary research question explores how AI can assist in the early detection and effective treatment of these conditions, potentially overcoming current systemic barriers in healthcare.
- Implementation of AI in smart cities to enhance healthcare services.
- Theoretical frameworks including Toffler’s Wave Approach and the concept of three identities.
- Technological foundations of AI, Machine Learning (ML), and the Internet of Things (IoT).
- Analysis of emotional and mental health monitoring via voice and facial recognition.
- Expert insights into the socio-cultural transformation and data privacy challenges in the era of AI.
Auszug aus dem Buch
3.4 The Correlation between Depression, Anxiety and Burn-out
When focusing on illnesses that can be diagnosed easily or the therapy can be adapted, most of the time, people think of a rash, Parkinson, etc., basically everything that is visually visible to the human eye. But what about all the silent illnesses, such as depression and anxiety (Powers, n.d.)? For the reason that these three illnesses are the most common ones to arise in today's society and the focus of the three hypotheses rely on depression, anxiety and burn-out — this chapter aims to analyse such diseases.
All individuals who suffer from depression, burn-out, anxiety have to reach out for help by themselves, which means first of all being diagnosed with these issues and secondly being treated within therapy or taking medication. For the reason that most of the time, subjects with such disorders shy away and do not make the first step to getting diagnosed because they are not motivated - new solutions have to be found (Powers, n.d.). Focusing on this problem, the National Institute of Mental Health indicates "that 37% adults" with these sicknesses do not even receive any diagnoses or treatment — mostly for the reason of health care systems (Powers, n.d.). This issue of not being diagnosed and no capabilities for treatments opens up another huge concern in regards to higher suicide rates, less productivity, etc.. When putting the focus of attention on these mental sicknesses in the U.S. — were depression, burn-out and anxiety are a massive issue — it becomes clear that around 43.8 million Americans suffer from at least one of these diseases (Mental Health By the Numbers | NAMI: National Alliance on Mental Illness, n.d.). The NAMI states that every fifth adult is suffering from a mental illness in one year, which means that these illnesses can reoccur, or an individual has been suffering for many years. For this reason, that every year the number of affected individuals is growing, the demand for therapy, doctors, etc. is rising from day to day.
Summary of Chapters
1.0 Introduction: Sets the foundation by defining the research question regarding AI’s role in improving quality of life and introduces the thesis hypotheses.
2.0 Methodology: Outlines the research approach, employing a triangular method consisting of literature review, case studies, and expert interviews.
3.0 Key Theory Definitions & Explanations: Establishes theoretical concepts, including Toffler’s Wave Approach and the influence of societal identity on AI interaction, alongside an analysis of mental health conditions.
4.0 The Technology behind Artificial Intelligence: Provides a deep dive into the technical aspects of AI, ML, IoT, and their implementation within the context of smart cities.
5.0 AI in Smart Cities with the Focus on Health: Examines practical applications of AI in the health sector, particularly for emergency response and mental health diagnosis.
6.0 Societal & Cultural Transformation: Analyzes the cultural shifts required for society to adapt to the "Era of AI" and overcome technological apprehension.
7.0 Expert Interview Analysis: Evaluates findings from expert interviews, confirming the theoretical potential of AI in healthcare while highlighting data privacy concerns.
8.0 Futuristic Outlook: Discusses the future trajectory of AI research and the importance of ethical, transparent implementation.
9.0 Discussion and Conclusion: Summarizes the thesis findings, confirming that AI has the potential to enhance diagnosis and treatment for mental health, provided that data privacy is addressed.
Keywords
Artificial Intelligence, AI, The Era of AI, Monitoring, Emotions, Mental Illnesses, Health Sector, Machine Learning, Internet of Things, Smart Cities, Data Privacy, Depression, Anxiety, Burn-out, Digital Identity.
Frequently Asked Questions
What is the core focus of this bachelor thesis?
The thesis examines how Artificial Intelligence and associated technologies can improve the quality of life, specifically by monitoring and managing mental health issues in modern, data-driven societies.
Which primary thematic areas are covered?
The paper covers the intersection of smart technology, urban development (smart cities), human-machine interaction, and the psychological impact of digital transformation on health.
What is the central research question?
The research is guided by the question: "How can Artificial Intelligence monitoring aid in the improvement of the quality of life in today's society?"
What scientific methods were utilized for the research?
The author employed a triangular research approach, combining a comprehensive literature review, analysis of exploratory case studies (such as MIT experiments), and semi-structured expert interviews.
What key topics are analyzed in the main body of the work?
The main body focuses on theoretical foundations like Toffler’s Wave Approach, the technical inner workings of AI/ML/IoT, specific applications in healthcare, and the societal challenges regarding data privacy and mindset shifts.
Which keywords best characterize this research?
The research is best characterized by terms such as Artificial Intelligence, Mental Illnesses, Health Sector, Monitoring, and Data Privacy.
How does AI specifically help in the context of mental health?
AI models can analyze speech patterns and voice biomarkers to detect signs of depression or anxiety, allowing for earlier diagnosis and more accessible treatment options, especially for individuals who might be hesitant to seek traditional therapy.
What are the major challenges for AI implementation in the health sector?
The primary challenges include data privacy concerns, the need for transparency, mistrust from older generations, and the requirement for robust cybersecurity to prevent data misuse.
- Arbeit zitieren
- Sina Kiene (Autor:in), 2020, Artificial Intelligence in order to facilitate Diagnoses and Treatment. The Opportunity Smart Cities give Subjects, München, GRIN Verlag, https://www.grin.com/document/994741
