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HQS590 Capstone Project
The Role of Artificial Intelligence in Early Disease Detection and Management in Saudi
Arabia: A Systematic Review
A Capstone Project
Submitted in Partial fulfillment of the
Requirements for the Degree of
Executive Master in Healthcare Quality and Patient Safety
Prepared by
Supervised:
Date
Declaration
I declare that the capstone project entitle (The Role of Artificial Intelligence in Early Disease
Detection and Management in Saudi Arabia: A Systematic Review) 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 ……………………………………………………………………………………………………… iviii
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 (Bhatt et al., 2021). 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 (AlDhabyani, 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 (Bhatt et al., 2021). 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 (Dabdoub et al., 2022). For instance, AIdriven 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 (Srivastava et al.,
2023). 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
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” (Koubaa et al., 2020).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 (Al Kuwaiti,2023). Integrating AI into
healthcare has consistently demonstrated its ability to deliver more efficient, faster, and costeffective care (Koubaa et al., 2020).
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 (Dabdoub et al., 2022). In pandemic management,
ChatGPT rapidly disseminates vital information. It serves as a virtual assistant in surgical
consultations, aids dental practices (Khanagar et al., 2022), simplifies medical education, and
aids in disease diagnosis (Dabdoub et al., 2022). Younis et al. examined the use of AI tools,
especially ChatGPT, within the healthcare sector, focusing on a variety of applications like
diagnosis, radiology, education, and research. The authors mentioned some advantages include
improved efficiency, tailored learning experiences, and swift communication of information.,
While, the challenges consist of biases, ethical dilemmas, and concerns regarding data privacy
(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). Elsabagh et al. highlighted the significant potential of artificial
intelligence (AI) in the diagnosis of sickle cell disease (SCD), the detection of its complications,
and the customization of treatment. AI tools enhance diagnostic accuracy, anticipate
complications such as organ dysfunction, and foster personalized care through effective risk
stratification. However, there are challenges that must be addressed, including limited
validation of AI technologies, a lack of diversity in datasets, and various legal considerations.
To achieve broader implementation, further research will be essential in overcoming these
obstacles. (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 (Dunworth, 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
(Aljondi et al., 2023).
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 (Dunworth, 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 (Koubaa et al., 2020).
Khanagar et al. examined the application of artificial intelligence (AI) in diagnosing and
predicting dental caries (DC). The authors found that AI models, particularly convolutional
neural networks (CNNs) and artificial neural networks (ANNs), significantly improve
diagnostic accuracy compared to traditional methods. The review highlights AI’s potential for
early detection and intervention, which could enhance patient outcomes and reduce healthcare
costs. However, the authors called for larger, standardized datasets to further validate these AI
applications in clinical practice (Khanagar et al., 2022). Alotaibi, Aldahash, and Mulyana
(2020) introduced an innovative healthcare analytics system in Saudi Arabia that utilizes big
data and social media. Their study focused on Sehaa, a tool designed to identify and analyze
symptoms and diseases from Arabic-language Twitter data. The research demonstrated the
tool’s scalability and effectiveness through the integration of Apache Spark and machine
learning algorithms. It identified prevalent health issues such as diabetes, hypertension, and
heart diseases, and provided valuable insights into the geographic distribution of disease
awareness and cases across major cities in Saudi Arabia (Alotaibi et al.,2020).
Dabdoub et al. explored the integration of artificial intelligence (AI) in Saudi Arabia’s
healthcare and biotechnology sectors, emphasizing its potential to revolutionize disease
diagnosis, drug discovery, and patient care. The review concluded by advocating for further
investment in education and infrastructure to maximize AI’s impact in Saudi healthcare,
underscoring the need for ongoing research and collaboration (Dabdoub et al., 2022).
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 (Iqbal et al., 2021).
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 (Hampiholi, 2024; Quazi, 2022).
Artificial Intelligence (AI) has significantly advanced early disease detection and management
in Saudi Arabia (Alotaibi et al.,2020). Several studies highlight its transformative impact on
healthcare practices within the Kingdom. A study by Alowais et al. emphasized 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 (Aljondi et al., 2023).
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).
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 (Softcircles, 2024). 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
Systematic Literature Review approach is recommended by researchers to identify the general
trends of any specific field of study (O’Cathain et al., 2010). The research design is widely
acceptable and applied in research community in every branch of social and applied sciences
(Sandelowski, et al., 2006). With minor differences to cover specific perspectives researchers
recommend to apply Preferred Reporting Items for Systematic Reviews and Meta-Analysis
(PRISMA) (Miake-Lye, et al., 2016; Popay, 2006). Systematic Literature Review (SLR) design
will be applied define the role of Artificial Intelligence in early detection of diseases and
preventive care management in Saudi Arabia. SLR approach is utilized to combine the previous
scholarly efforts in a specific domain. The approach is recommended to produce comprehensive
and unbiased synthesis of existing scholarly published literature in credible journals of relevant
domains following a standardized systematic literature protocols. PRISMA guidelines will be
followed to ensure adequate methodological rigor. SLR design enable the researcher to identify,
analyze, and integrate relevant and peer reviewed articles, reports and gray literature to provide
a consolidated and comprehensive conclusion to move forward in the relevant area of study.
PRISMA
Step 1: Identification:
I: Scholarly Databases, Articles will be identified from:
PubMed, Scopus, IEEE Xplore, Web of Science, Google Scholar (n = XXX)
II: Local databases, Records will be identified from:
Saudi Digital Library, institutional repositories (n = XXX)
Total number of Articles identified = (n = I + II)
Total Articles identified (n = XXX)
Articles filtered after removing duplicates (n = xx)
Step 2: Screening of Articles
Articles Screened for inclusion criteria (n =xxx)
– Relevance to AI in early disease detection
– Focus on Saudi Arabian population
Articles excluded after abstract/title review (n = XXX)
Reasons:
– Irrelevant to topic (for instance outside Saudi Arabia, non-healthcare or key words
for this research)
– Editorials, commentaries, opinion pieces
Step 3: Articles Eligibility
Full-text articles assessed for eligibility (n = XXX)
Peer-reviewed articles, reviews, case studies
Published between January 2014–December 2024
Articles excluded after full-text review (n = XXX)
Reasons:
– Methodological inadequacy
– Incomplete data or inaccessible full text
– Not focused on AI in disease detection
Step 4:
Inclusion of Articles and Analysis
Studies included in qualitative synthesis (n = XXX)
– Key findings summarized narratively
Studies included in quantitative synthesis
– Only if meta-analysis is feasible (n = XXX)
Research Setting
This setting of this study is academic in nature and proposed to investigate healthcare ecosystem
of Kingdom of Saudi Arabia. The study will emphasize on research publications, reports and
case studies on integration of Artificial Intelligence in preventive diagnosis and management
within the geographical boundaries of Kingdom. Well recognized databases including PubMed,
Google Scholar, Web of Science and IEEE explore are proposed to be included in this SLR.
However further databases may also be included after extensive literature review and with
recommendations of research supervisor. Initially a range of 10 years is proposed and articles
published from Jan, 2014 to December 20124. Furthermore local databases and repositories
managed by respective authorities and government institutions will be included. Saudi Digital
Library and repositories managed by healthcare organizations will be explored for collecting
data specifically targeting Saudi region.
Inclusion Criteria
Inclusion criteria given in PRISMA will be enriched to ensure relevance and quality. The
articles selected must meet following criteria.
1. Relevance:
The Articles must focus on AI applications in medical and healthcare however studies
addressing early disease detection and preventive healthcare using AI will be preferred. Since
there are possibilities that articles with such a stringent inclusion criteria may not be published
hence geographic condition may be changed to include relevant articles and enrich analysis
based on global developments in the area of AI application in preventive healthcare and
management.
2. Population:
This study proposed specifically to understand developments of AI in Saudi Healthcare system
and studies engaging Saudi population will be preferred but to increase application of
developments in the area globally will also be included. But the facts, figures and infrastructure
related with AI developments in KSA will be recorded and included for quantitative analysis.
3. Study Types:
Scholarly written and Peer-reviewed research articles published in PubMed, Scopus, IEEE
Xplore, Web of Science, Google Scholar and other relevant but well reputed journals and
conferences of international repute will be included in this study in last 10 years i.e between
January 2014–December 2024. Full text of articles must be available for any study to be
included in proposed study.
Exclusion Criteria
In addition to the PRISMA criteria following are criteria will be used to filter out from the scope
of proposed research study:
1. Irrelevance:
Studies that do not have any key word in their research title and abstract will not be included in
this study, however AI innovations in prediction modeling may be included where required.
2. Study Design:
Editorials, blogs posts, general commentaries on different online forums, opinion pieces, and
studies lacking in scientific research methods and rigor will not be included in proposed study.
3. Duplication:
Studies having similar data and reporting and identical conclusions will not be included to
remove qualitative and quantitative skewedness of the results.
4. Incomplete Information:
Articles with conclusions not supported with adequate evidence and unavailable with full text
will not be included in this study.
5. Language Barrier:
Articles published in any language other than English will not be included in this study.
Data Collection Tools (Proposed)
The proposed study is based on systematic literature review hence conventional tools for
instance questionnaire development or structured, semi structured or open ended questionnaire
method will not be applicable to collect data. The study will be based on secondary data that is
scholarly published articles, in international credible journals and databases. Electronic
databases including PubMed, Scopus, IEEE Explore, Google Scholar and Web of Science will
be primary databases from where researcher will collect and develop a database of articles to
be thoroughly studied and analyzed using appropriate data analysis tools. Boolean Operators
will be utilized to narrow down the articles. Boolean Operators are widely utilized in SLR for
narrowing down large number of articles to extract more relevant and exact information specific
to the topic under study (Popay, 2006). Primary Boolean Operators includes the logic of And
or Not and quotation marks or parenthesis are used for developing advanced logic for accurate
data collection from large number of available resources. Early Disease Detection, Preventive
healthcare, healthcare management, Saudi Arabia, AI application in early disease detection, and
synonyms of these terminologies will be utilized to apply Boolean Operators and to develop
advance logic (Tashakkori, et al., 2020). Citation management tools including EndNote or
Zotero will be applied to manage intext citation and to remove duplicated articles. However
Microsoft Office reference may also be used for this purpose. Data will be screened using
Microsoft Excel spreadsheets where annotated bibliography will be developed for classifying
articles with respect to title, year, authors, objectives, methods, outcomes, and key findings
related to AI in healthcare and early disease detection in KSA
Pilot Study
Pilot study method is generally applied in research studies where researcher collect primary
data directly from qualified respondents (Popay, 2006). Since the proposed study will be based
on secondary data resources hence pilot study may not be required in proposed study. However
to test a trial run to estimate the number of articles published on initially designed PRISMA and
inclusion and exclusion criteria will be carried out. If the available number of articles are below
20 than scope of study may be expanded in consultation with supervisor.
Data Analysis
The data will be analyzed using narrative synthesis approach. Themes will be identified based
on recurrence of patterns of key words and terminologies relevant to the topic of study
including but not limited to machine learning, deep learning, Artificial Intelligence, Preventive
healthcare, early detection etc. Furthermore quantitative data will be analyzed using descriptive
statistical analysis. Findings from the data will be presented using graphs and visuals and tables
to give a clear picture of the current state of AI in early detection of diseases in KSA for better
and quick clarity to the reader of this research study. Furthermore, thematic analysis with
descriptive statistics, Meta inference and triangulation methods will be applied in proposed
study for data analysis (O’Cathain et al., 2010; Tashakkori, et al., 2020). These methods are
widely applied in systematic literature reviews and subsequent nature of data filtered out
according to inclusion, exclusion criteria and PRISMA (Thomas & Harden, 2008; Popay,
2006).
Chapter Four
Results
Results
Chapter Five
Discussion
Discussion
Conclusion
Recommendations
References
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Appendixes
Appendix A
Appendix B
Appendix C
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