Description
HQS590 Capstone Project
Title of project
A Capstone Project
Submitted in Partial fulfillment of the
Requirements for the Degree of
Executive Master in Healthcare Quality and Patient Safety
Prepared by
Student Name/s
Supervised:
Dr. …………………..
Date
Declaration
I declare that the capstone project entitle (Put your title) submitted to the Saudi
Electronic University is my own original work. I declare that the capstone project does not
contain material previously published or written by a third party, except where this is
appropriately cited through full and accurate referencing. I declare that the Saudi Electronic
University has a right to refuse the capstone project if contains plagiarism and cancel the
capstone project at any time and the student has the full responsibility regarding any further
legal actions.
Acknowledgement
Table of Contents
Declaration ………………………………………………………………………………………………………………… ii
Acknowledgement……………………………………………………………………………………………………… iii
Table of Content ………………………………………………………………………………………………………. ivii
List of Abbreviations…………………………………………………………………………………………………….v
List of Tables……………………………………………………………………………………………………………….v
List of Figures ……………………………………………………………………………………………………………. ii
List of Appendixes …………………………………………………………………………………………………….. iii
Abstract …………………………………………………………………………………………………………………… iix
Chapter 1: Introduction ………………………………………………………………………………………………..1
Chapter 2: Literature Review …………………………………………………………………………………………4
Chapter 3: Objectives ………………………………………………………………………………………………….10
Chapter 4: Materials and Methods ………………………………………………………………………………..12
Chapter 5: Results ………………………………………………………………………………………………………17
Chapter 6: Discussion ………………………………………………………………………………………………..27
Conclusion…………………………………………………………………………………………………………………32
Recommendations ………………………………………………………………………………………………………34
References …………………………………………………………………………………………………………………36
Appendixe A “Study questionnaire ” …………………………………………………………………………….42
Appendixes B ” Declaration form ” ………………………………………………………………………………42
Appendixes C “IRB Approval form ” ……………………………………………………………………………42
Table of Tables
S. No.
Contents
Page No.
Table 5-1
Name of the table
18
Table 5-2
Name of the table
19
Table 5-3
Name of the table
20
Table 5-4
21
Table 5-5
22
Table 5-6
23
Table 5-7
24
Table 5-8
25
Table 5-9
25
Table 5-10
26
Table 5-11
26
List of Appendixes
No.
Contents
Page No.
Appendix A
Questionnaire
43
Appendix B
Consent Form and IRB approval form
44
Appendix C
Plagiarism report
List of Abbreviations
All of the following abbreviations are to be taken in context of the study
A
B
C
Abstract
It should be about (200-250) word
Chapter One
Introduction and Objectives
Introduction
Artificial Intelligence (AI) has emerged as a transformative technology with vast potential to
revolutionize healthcare systems worldwide. In recent years, AI’s applications in disease
detection and management have garnered significant attention, particularly in the context of
early diagnosis, personalized treatment plans, and predictive analytics. This technological
evolution is particularly relevant to Saudi Arabia, where the healthcare sector is undergoing
rapid modernization to meet the demands of a growing population, increasing prevalence of
chronic diseases, and the need for more efficient healthcare delivery (Al-Dhabyani, 2020).
AI can enhance early disease detection through the use of machine learning algorithms, which
can analyze vast amounts of medical data to identify patterns not readily apparent to human
clinicians. Early detection is crucial in diseases such as cancer, diabetes, and cardiovascular
conditions, where timely intervention can significantly improve patient outcomes and reduce
healthcare costs (Jensen et al., 2020). In Saudi Arabia, where the prevalence of chronic diseases
is rising at an alarming rate, AI-based diagnostic tools are seen as a potential solution to alleviate
the pressure on healthcare resources and improve the quality of care (Al-Ghamdi et al., 2019).
Moreover, AI’s integration into healthcare management is not limited to diagnosis alone but
extends to predictive analytics, where it assists in forecasting disease outbreaks, monitoring
patient health trends, and optimizing treatment plans. For instance, AI-driven tools in
telemedicine can remotely monitor patients with chronic conditions and provide real-time
feedback to healthcare providers, thus improving disease management outside traditional
clinical settings (Alzahrani, 2021). Furthermore, Saudi Arabia’s Vision 2030 initiative
emphasizes the adoption of cutting-edge technologies like AI to enhance the efficiency and
accessibility of healthcare services, positioning AI as a key enabler in the country’s healthcare
future (Saudi Vision 2030, 2016).
Despite the promising potential, there are challenges to implementing AI solutions in Saudi
Arabia, including data privacy concerns, a shortage of skilled AI professionals, and the need
for regulatory frameworks that ensure the ethical use of AI in healthcare. This systematic review
aims to examine the current state of AI in early disease detection and management in Saudi
Arabia, explore its benefits and challenges, and provide recommendations for its future
integration into the national healthcare system.
Objectives
How can Artificial Intelligence technologies contribute to improving early disease
detection in Saudi Arabia?
What are the challenges facing the implementation of Artificial Intelligence in
managing chronic diseases within the Saudi healthcare system?
How does Saudi Arabia’s Vision 2030 influence the adoption of Artificial Intelligence
in healthcare for early disease detection and management?
Chapter Two
Literature Review
L Literature Review
AI and machine learning have revolutionized healthcare, especially in response to the
challenges posed by the COVID-19 pandemic, which strained global healthcare systems. These
technologies are increasingly incorporating innovations like the Internet of Things (IoT),
evolving into what is now termed the “Intelligence of Things.” This transformation highlights
how data-driven insights can reshape processes, behaviors, and values in healthcare. Smart
medical technologies powered by AI have gained public approval due to their alignment with
the 4P model of medicine—predictive, preventive, personalized, and participatory—thereby
enhancing patient autonomy. Integrating AI into healthcare has consistently demonstrated its
ability to deliver more efficient, faster, and cost-effective care. .( Al Kuwaiti,2023)
Artificial intelligence (AI) has emerged as a transformative force in various sectors, including
medicine and healthcare. Large language models like ChatGPT showcase AI’s potential by
generating human-like text through prompts. ChatGPT’s adaptability holds promise for
reshaping medical practices, improving patient care, and enhancing interactions among
healthcare professionals, patients, and data. In pandemic management, ChatGPT rapidly
disseminates vital information. It serves as a virtual assistant in surgical consultations, aids
dental practices, simplifies medical education, and aids in disease diagnosis. A total of 82 papers
were categorised into eight major areas, which are G1: treatment and medicine, G2: buildings
and equipment, G3: parts of the human body and areas of the disease, G4: patients, G5: citizens,
G6: cellular imaging, radiology, pulse and medical images, G7: doctors and nurses, and G8:
tools, devices and administration. Balancing AI’s role with human judgment remains a
challenge. A systematic literature review using the PRISMA approach explored AI’s
transformative potential in healthcare, highlighting ChatGPT’s versatile applications,
limitations, motivation, and challenges. In conclusion, ChatGPT’s diverse medical applications
demonstrate its potential for innovation, serving as a valuable resource for students, academics,
and researchers in healthcare. Additionally, this study serves as a guide, assisting students,
academics, and researchers in the field of medicine and healthcare alike.( Younis,2024)
Artificial intelligence (AI) is rapidly becoming an established arm in medical sciences and
clinical practice in numerous medical fields. Its implications have been rising and are being
widely used in research, diagnostics, and treatment options for many pathologies, including
sickle cell disease (SCD). AI has started new ways to improve risk stratification and diagnosing
SCD complications early, allowing rapid intervention and reallocation of resources to high-risk
patients. We reviewed the literature for established and new AI applications that may enhance
management of SCD through advancements in diagnosing SCD and its complications, risk
stratification, and the effect of AI in establishing an individualized approach in managing SCD
patients in the future. Aim: to review the benefits and drawbacks of resources utilizing AI in
clinical practice for improving the management for SCD cases(Elsabagh,2023)
In a study conducted by researchers from King Abdullah University of Science and Technology
(KAUST) and amplifAI health, an AI-based model was developed for the early detection of
diabetes using hyperspectral imaging. This method demonstrated significant potential in
identifying early signs of diabetic complications, reducing healthcare costs, and improving
patient outcomes (KAUST, 2024).
Saudi researchers introduced MiniGPT-Med, an AI-powered diagnostic tool aimed at
enhancing clinical decision-making. This system showed notable efficiency in accurately
identifying symptoms and supporting medical professionals in diagnosing diseases, thereby
complementing traditional diagnostic methods (Arab News, 2024).
Another study evaluated the application of AI in breast cancer screening among Saudi women.
The AI system exhibited high accuracy in detecting malignancies and reducing false positive
and negative results, offering a reliable alternative to conventional mammography screenings
(MDPI, 2024).
The collaboration between the Saudi Data and Artificial Intelligence Authority (SDAIA) and
Philips focused on integrating AI into the healthcare system to align with Vision 2030
objectives. This initiative aimed to enhance healthcare efficiency and patient care through
innovative AI-driven solutions (Philips, 2021).
In the domain of chronic disease management, the healthcare startup SDM utilized AI
technologies to improve the detection and management of conditions such as diabetes and
hypertension. By analyzing patient data, the AI system facilitated early intervention and
personalized treatment, leading to better patient monitoring and outcomes (Arab News, 2024).
An IoT-based AI framework known as “Smart Palm” was developed to combat Red Palm
Weevil infestations in Saudi Arabia. The system employed sensors and machine learning
algorithms to monitor palm tree health, enabling early detection and intervention to prevent
extensive agricultural damage (arXiv, 2019).
The Centre for Healthcare Intelligence at King Faisal Specialist Hospital & Research Centre
(KFSHRC) initiated efforts to integrate AI technologies into healthcare services. These
initiatives targeted enhanced diagnostic accuracy, streamlined hospital management, and
improved healthcare delivery across the Kingdom (KFSHRC, 2024).
At King Abdulaziz University, the Centre of Artificial Intelligence in Precision Medicine
applied AI algorithms to genomic research. This initiative focused on identifying
pharmacological targets through the analysis of Saudi patients’ genomic, transcriptomic, and
proteomic profiles, paving the way for precision medicine and personalized treatment
approaches (CAIPM, 2024).
KAUST (2024). Early diabetes detection with hyperspectral imaging. Retrieved from
smarthealth.kaust.edu.sa.
Artificial Intelligence (AI) has significantly advanced early disease detection and management
in Saudi Arabia. Several studies highlight its transformative impact on healthcare practices
within the Kingdom.
A study by Alowais et al. emphasizes AI’s role in clinical practice, showcasing its potential to
enhance diagnostic accuracy and patient care. This research illustrates how AI algorithms
process vast datasets to support early disease detection and personalized treatment (Alowais et
al., 2023).
In breast cancer detection, a study published in Applied Sciences examines the application of
AI in mammographic screenings among Saudi women. The findings highlight AI’s ability to
improve early breast cancer diagnosis while reducing false-positive mammography results,
ensuring more accurate and reliable screening (MDPI, 2024).
Further, researchers have investigated the integration of AI in healthcare systems to support
disease diagnosis. AI’s capability to process complex medical data aids clinicians in providing
accurate and timely diagnoses (Alowais et al., 2023,).
The collaboration between King Abdullah University of Science and Technology (KAUST)
and amplifAI health introduced a novel system combining AI with hyperspectral imaging for
early-stage disease detection. This approach exemplifies innovative medical diagnostics
powered by AI technology (KAUST, 2024).
In the agricultural context, a study titled “Dates Fruit Disease Recognition using Machine
Learning” explores AI applications for managing diseases affecting date fruits. The findings
demonstrate how machine learning algorithms accurately identify and classify diseases,
providing effective solutions for agricultural management (ArXiv, 2023).
These studies collectively underscore the pivotal role of AI in revolutionizing early disease
detection and management in Saudi Arabia, contributing to improved healthcare outcomes and
operational efficiency. AI in Diagnostic Radiology: AI-powered systems have been
instrumental in enhancing diagnostic radiology in Saudi Arabia. These tools have enabled high
diagnostic accuracy, especially in lung cancer screening and the classification of lung nodules.
AI’s integration into radiology has shown promising results, especially when used alongside
expert radiologists (Aljerian et al., 2022)
AI applications have revolutionized the healthcare sector, as explored in this comprehensive
literature review. Key areas of focus include: (a) medical imaging and diagnostics, (b) virtual
patient care, (c) medical research and drug discovery, (d) patient engagement and compliance,
(e) rehabilitation, and (f) administrative functions. AI has proven impactful in early disease
detection, controlling COVID-19 outbreaks, improving virtual care, managing electronic health
records, and streamlining administrative tasks. Additionally, it aids in drug and vaccine
development, detects prescription errors, supports large-scale data analysis, and enhances
rehabilitation processes. Despite these advancements, challenges persist, particularly in
privacy, safety, and ethical considerations related to self-determination.( Al Kuwaiti,2023)
Chapter Three
Materials and Methods
Materials and Methods
Study Design
This study adopts a systematic review design to critically analyze and synthesize the existing
literature on the role of Artificial Intelligence (AI) in early disease detection and management
in Saudi Arabia. A systematic review methodology was chosen to provide an in-depth
understanding of the current state of AI applications in healthcare, identifying trends, gaps, and
challenges in the existing body of research. The review follows standard protocols to ensure
transparency and reproducibility.
Setting
The study encompasses research conducted within the healthcare settings of Saudi Arabia. It
includes studies from hospitals, clinics, research institutions, and AI-focused healthcare
companies across the country. The review includes both public and private sector healthcare
systems, reflecting the diverse use cases of AI in early disease detection and management in
the Saudi healthcare environment.
Sample Size and Technique
Since this study is a systematic review, the concept of a traditional sample size does not apply.
Instead, the review aims to include all relevant studies published in peer-reviewed journals,
conference proceedings, and other reputable sources. A comprehensive literature search will
be conducted using multiple databases such as PubMed, Scopus, Google Scholar, and IEEE
Xplore. The inclusion criteria will ensure that only studies related to AI applications in early
disease detection and management in Saudi Arabia are considered.
The inclusion and exclusion criteria outlined below will guide the selection of studies for
review.
Inclusion Criteria
1. Studies that focus on the application of AI in early disease detection or disease
management in Saudi Arabia.
2. Research articles published in English or Arabic.
3. Studies that address AI-based technologies, such as machine learning, deep learning,
or AI-powered diagnostic tools.
4. Empirical studies, clinical trials, or review articles related to AI and healthcare in
Saudi Arabia.
5. Studies published between 2010 and 2024 to capture the most recent advancements.
Exclusion Criteria
1. Studies that do not focus on AI applications in healthcare or disease management.
2. Studies conducted outside of Saudi Arabia or those not related to the Saudi healthcare
system.
3. Articles written in languages other than English or Arabic.
4. Opinion papers, editorials, and non-peer-reviewed articles.
5. Studies that do not specifically address early disease detection or management.
Data Collection Tools
Data collection will involve a systematic literature search across several academic databases.
A structured data extraction form will be used to collect key information from each selected
study, including:
•
Study title, authors, and publication year
•
Study objectives
•
AI techniques used (e.g., machine learning, deep learning)
•
Disease types addressed (e.g., cancer, diabetes, cardiovascular diseases)
•
Findings related to the effectiveness of AI in early detection and management
•
Methodology used (e.g., diagnostic accuracy, case studies, pilot studies)
•
Challenges identified in the integration of AI into healthcare
Pilot Study
To assess the reliability and comprehensiveness of the data collection tools, a pilot study will
be conducted on a small subset of articles (5-10 studies). This preliminary step will ensure
that the data extraction form effectively captures all relevant information and allows for any
necessary adjustments before proceeding with the full literature review.
Validity and Reliability
To ensure the validity and reliability of the systematic review, the following measures will be
taken:
•
Inclusion criteria will be clearly defined, ensuring that only studies that directly
address the research questions are included.
•
Multiple reviewers will independently evaluate and extract data from each selected
study to minimize bias. Discrepancies between reviewers will be resolved through
consensus or a third-party mediator.
•
Inter-rater reliability will be assessed using the Kappa coefficient, which measures the
agreement between reviewers.
•
The search process will be conducted systematically, using multiple relevant databases
to ensure comprehensive coverage of the literature.
Data Analysis
Data will be analyzed using a qualitative synthesis approach. The studies will be categorized
based on the type of AI technology used (e.g., machine learning, deep learning), disease areas
(e.g., cancer, diabetes), and the outcomes assessed (e.g., diagnostic accuracy, patient
management improvements). Key themes and patterns in the literature will be identified, with
a focus on the effectiveness of AI in early disease detection and management within the Saudi
healthcare context. The results will be presented narratively, supported by descriptive
statistics where applicable (e.g., percentages of studies showing positive outcomes).
In cases where the studies are sufficiently homogeneous, meta-analysis may be performed to
calculate pooled effect sizes and assess the overall impact of AI in disease detection and
management.
Ethical Considerations
As this study is a systematic review of published literature, ethical approval is not required.
However, ethical considerations regarding data privacy and the responsible use of AI in
healthcare will be acknowledged. The study will include only publicly available data from
reputable, peer-reviewed sources, ensuring that all included research adheres to ethical
standards. Additionally, attention will be given to the ethical implications of AI in healthcare,
such as concerns about data privacy, algorithmic bias, and equity in AI-driven healthcare
services, especially in the Saudi context.
Chapter Four
Results
Results
Chapter Five
Discussion
Discussion
Conclusion
Recommendations
References
Al Kuwaiti, A.; Nazer, K.; Al-Reedy, A.; Al-Shehri, S.; Al-Muhanna, A.; Subbarayalu, A.V.;
Al Muhanna, D.; Al-Muhanna, F.A. A Review of the Role of Artificial Intelligence in
Healthcare. J. Pers. Med. 2023, 13, 951. Younis, H.A.; Eisa, T.A.E.;
Nasser, M.; Sahib, T.M.; Noor, A.A.; Alyasiri, O.M.; Salisu, S.; Hayder, I.M.; Younis,
H.A. A Systematic Review and Meta-Analysis of Artificial Intelligence Tools in
Medicine and Healthcare: Applications, Considerations, Limitations, Motivation and
Challenges. Diagnostics 2024, 14, 109. https://
doi.org/10.3390/diagnostics1401010910.3390/jpm13060951
Al-Dhabyani, W. (2020). Machine learning applications in early disease detection: A review
of healthcare trends in Saudi Arabia. Saudi Medical Journal, 41(5), 463–472.
Al-Ghamdi, M. A., Al-Rashed, R., & Khan, M. S. (2019). Challenges and opportunities of AI
in healthcare: A Saudi perspective. International Journal of Healthcare Informatics,
45(2), 175–190.
Aljerian, N., et al. (2022). Artificial Intelligence in Healthcare and its Application in Saudi
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Alowais, R., et al. (2023). Artificial Intelligence in Healthcare. Retrieved from Wikipedia.
Alzahrani, A. (2021). Leveraging AI for remote patient monitoring in Saudi Arabia: A
telemedicine approach. Journal of Telemedicine and Telecare, 27(4), 256–265.
Applied Sciences. (2024). AI in Breast Cancer Screening: Enhancing Diagnosis Accuracy in
Saudi Arabia. Retrieved from MDPI.
Arab News (2024). MiniGPT-Med and chronic disease management. Retrieved from
arabnews.com.
.arXiv (2019). Smart Palm for Red Palm Weevil detection. Retrieved from arxiv.org.
ArXiv. (2023). Dates Fruit Disease Recognition using Machine Learning. Retrieved from
arXiv.
CAIPM (2024). Genomic medicine advancements. Retrieved from caipm.kau.edu.sa.
Centre for Artificial Intelligence in Precision Medicine (CAIPM). (2024). Genomic Medicine
Powered by AI. Retrieved from CAIPM.
Elsabagh, A. A., Elhadary, M., Elsayed, B., Elshoeibi, A. M., Ferih, K., Kaddoura, R.,
Alkindi, S., Alshurafa, A., Alrasheed, M., Alzayed, A., Al-Abdulmalek, A., Altooq, J.
A., & Yassin, M. (2023). Artificial intelligence in sickle disease. Blood Reviews, 61,
101102. 10.1016/j.blre.2023.101102
Iqbal, S., Ahmad, S., & Bano, B. (2021). A Systematic Review: Role of Artificial Intelligence
During the COVID-19 Pandemic in the Healthcare System. International Journal of
Intelligent Information Technologies, 17(1).
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Jensen, P. B., Jensen, L. J., & Brunak, S. (2020). Artificial intelligence and personalized
medicine: Implications for healthcare transformation. The Lancet Digital Health, 2(3)
KFSHRC (2024). Centre for Healthcare Intelligence initiatives. Retrieved from kfshrc.edu.sa.
King Abdullah University of Science and Technology (KAUST). (2024). KAUST and
amplifAI Health Combine Technologies for Early Diabetes Detection. Retrieved from
KAUST.
King Faisal Specialist Hospital & Research Centre (KFSHRC). (2024). AI Integration in
Healthcare: Advancements and Challenges. Retrieved from KFSHRC.
MDPI (2024). AI in breast cancer screening. Retrieved from mdpi.com.
Philips (2021). SDAIA-Philips partnership for AI in healthcare. Retrieved from philips.sa.
Saudi Data and Artificial Intelligence Authority (SDAIA). (2021). Transforming Healthcare
Through AI in Saudi Arabia. Retrieved from Philips.
Saudi Vision 2030. (2016). Saudi Vision 2030: Realizing tomorrow’s opportunities. Retrieved
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Appendixes
Appendix A
Appendix B
Appendix C
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