Leveraging Artificial Intelligence to Achieve Sustainable Public Healthcare Services in Saudi Arabia: A Systematic Literature Review of Critical Success Factors

Abbreviations

1  Introduction

Artificial Intelligence (AI) rapidly improves the healthcare industry across the global frontier with innovations designed to improve public healthcare [1]. In the international landscape, artificial intelligence enhances disease monitoring, data predicting, patient care, health and service organization, and aids policy decisions [2]. AI is employed in Saudi Arabia to improve health care and supports the Vision 2030 project [3]. AI strengthens epidemiology by estimating the occurrence of diseases using forecasting and defining the existing threats concerning the generally prevalent and typical regional genetic and lifestyle diseases [4]. In environmental health, AI tracks and controls the ecological impacts of influx and increase in urban and industrialization processes to promote safer living standards. AI significantly affects health services administration in the Kingdom as it helps solve administrative issues, distribute resources, and increase effectiveness while providing services to clients [5]. It also supports social and behavioral sciences by analyzing data to develop culturally appropriate interventions for public health problems [6,7]. In addition, AI enhances effective and timely formulation of preventive measures, eventually leading to effective health policies and fortification of the international health care systems, particularly for mass events like pilgrimage, hajj, and community as well as occupational health by enhancing personalized health care delivery and quality occupational health [8]. As remarkable as the advancements in AI are practical, the integration of these advances in Saudi Arabia is based on tenets derived from Islamic ethical standards that mandate pertinent AI applications to be free from bias, opaque, and culturally insensitive [9]. With the further advancement of AI, improving public health in Saudi Arabia would be more profound, which would help Saudi Arabia achieve the set healthcare objectives and fight future sicknesses more efficiently. Various factors play critical roles in addressing public health concerns in Saudi Arabia, viz., effective healthcare policies, investment in medical infrastructure, access to quality healthcare services, public awareness, and education while integrating with AI, data governance, and cultural sensitivity in healthcare delivery [10,11]. The healthcare system is also responsible for countering communicable and non-communicable diseases, with significant emphasis on diseases prevailing in the region [12].

Environmental health is essential to an increased rate of urbanization, industrialization, and climate change, which calls for attention to safe water supply, pollution, and waste disposal [13]. Health services administration aims to enhance the quality of health services across the country using advanced technologies such as telecommunications to deliver health services [6]. In addition, the ethical principles of public health connected with the Islamic culture and Tradition of locality ensure human rights for the benefit of the majority of the population. This review focuses on how Saudi Arabia applies and adapts the international concept of public health to fit its uniquely designed system [14]. Based on the identified research gap from the previous research, there is a dearth of relevant literature on public health and the relevance, scope, and usage of AI specific to Saudi Arabia. Thus, conducting a systematic literature review on leveraging Artificial Intelligence plays a critical role in improving public health performance in Saudi Arabia from the perspective of both academics and industry [15]. AI integration in Saudi Arabia’s healthcare system must align with Islamic values and cultural norms, emphasizing privacy, dignity, equity, and community welfare. AI should uphold patient privacy while using a patient’s data, which should be masked and stored securely and ethically to conform to halal standards. Patients’ self-determination must be served using AI. AI-driven telemedicine maintains justice and equitable access to practice quality-generated medical instructions to remote and underserved clients. Explainability reduces accountability and maintains transparency. The concept of maslaha as community well-being is maintained through prediction, disease watchdog, and resource management for social health welfare. Gender sensitivity is integrated into creating AI applications to adapt to such cultural norms, including same-gender calls in telemedicine. These principles combine to establish AI’s set Norms and Values to conform with Islam and Culture, Vision 2030 objectives, and Intent to develop trust and improve healthcare [16].

The research review is crucial as it aims to fill some existing research gaps. Firstly, the application of AI is becoming paramount in Saudi Arabia’s healthcare sector, acting as a force multiplier with significant capacity in data, diagnosis of diseases, and improvement of the service delivery process [17]. Second is Saudi Arabia’s public health concern, which is experiencing changes in its public health systems due to Vision 2030. Thus, this study will seek to discover how AI can be the most effective when applied in public health, the unique capabilities, and the phases that can benefit most from AI applications [18,19]. This systematic literature review provides a thorough and linear evaluation of past research, analysis of existing research, and perception of the lack of knowledge of AI applications in public health entities in Saudi Arabia. The research outcomes help tailor AI solutions to a more sustainable, socio-economic, and culturally viable healthcare environment in Saudi Arabia [20,21]. In addition, it is a scientific contribution to healthcare industry-specific recommendations based on secondary research documents that can be helpful for policymakers, healthcare managers, and researchers embarking on the systematic review process [22]. Thus, the systematic review can be described as an important scholarly work contributing to the role and development of AI within Saudi Arabia’s public health sector more efficiently and ethically.

2  Review of Literature

2.1 Artificial Intelligence and Public Health in Saudi Arabia

Globally, Artificial Intelligence (AI) integration into public health is an emerging field that can transform health systems. AI is highly relevant to public health in Saudi Arabia, as the country is currently revitalizing its healthcare sector in alignment with the Vision 2030 plan [23,24].

This review aims to discover the state and the use of artificial intelligence for public health in Saudi Arabia with a perspective on the specificities of the Saudi environment. Healthcare and epidemiology are the fields in which AI is applied in Saudi Arabia. The Kingdom of Saudi Arabia (KSA) encounters numerous public health challenges, some unique to the nation due to the millions of people who enter the country annually for pilgrimage during hajj, which is a significant concern [25]. Actions such as remote health surveillance, advanced epidemiological modeling, and local and regional outbreak prediction are currently utilized to aid in planning and executing large-scale health interventions, often during mass congregations [26]. These models increase Saudi Arabia’s capacity to prevent and control infections and diseases and strengthen the health system responses [27]. Thus, AI is transforming the public healthcare systems in Saudi Arabia [28]. AI technologies contribute greatly to improving the performance of operations and resource utilization in healthcare by tackling key sectors. Patient inflow, seasonal demand, and resources are predicted using predictive analytics to optimally address staff, bed, and equipment usage during surges such as Hajj. AI improves the staff schedule by detecting busy hours and work on the shift so that it will not be overstaffed or understaffed. Materials management systems forecast scarcity of inventory and alert purchasing to reorder material to ensure patients get needed supplies without overstocking. Patients are properly managed using scheduling and queueing services from the AI utility, and the patients’ waiting time is also reduced. Information technologies in imaging diagnostics help speed up the analysis of cases so that specialists concentrate on complicated cases. AI equally reduces the idle time in the operating room and improves the efficiency of the surgeons’ time in the operating room. Smart energy consumption systems lower overhead expenses by efficiently regulating climate by controlling a facility’s needs towards sustainable practices [29]. In emergencies, AI learns how much resource is required and helps match ICUs, ventilators, and staff in a shorter time. Predictive maintenance helps avoid equipment failure, and important devices are always on and ready to use [30]. Electronic health records enhanced by artificial intelligence enhance case categorization and data retrieval and automation of redundant administrative procedures, such as billing and coding, that are time-consuming and likely to contain errors at a substantial cost. All these applications improve healthcare delivery and the effectiveness and sustainability of healthcare systems. Saudi Arabia’s public healthcare system can increase efficiency, reduce costs, and improve the quality of care provided to patients by incorporating these AI-powered resource management strategies, all of which are crucial for achieving the goals of Vision 2030. AI models rely on data from Electronic Health Records (EHRs) and other macro/micro databases to recognize high-risk populations and quickly address every community’s needs [31,32]. This approach is beneficial for chronic diseases, which remain the primary health concern in Saudi Arabia. For instance, AI can be employed to develop effective promotional strategies to address issues such as diabetes and obesity in specific countries relative to their diet, which can encourage those diseases [33]. The utilization of AI in Saudi Arabian health policy decision-making has increased. Decision-makers use AI analysis to make more intelligent choices about where to invest, what healthcare services to offer, and which public health issues should be addressed [34]. Using such an approach, AI helps be more proactive regarding collaborative planning and forecasting the future demand in public health enterprises [35]. Given various operational and strategic challenges, it helps to prepare and plan an extension of the healthcare delivery system. Thus, this study review uses systematic analysis of the role of Artificial Intelligence in advancing public healthcare performance in Saudi Arabia. It critically reviews the pivoting contribution of AI in leveraging public health in Saudi Arabia using past studies broadly covering use cases, best practices, academic research papers, and reviews of AI disruption in the public healthcare domain. Considering the emerging usage of AI across various industries, its application, implementation challenges, and strategic solutions for public healthcare should be thoroughly reviewed. Although extending previous literature, this review has many unique contributions. Firstly, it provides a logical, well-structured, and innovative categorization of past research according to its potential uses, limitations, and recommendations. Secondly, the authors proposed a framework for information synthesis to highlight potential AI implementation challenges that require scholarly investigation to enhance the state of knowledge at this time based on the results of the systematic literature review (SLR). The review will endeavor to give a comprehensive view of how AI is applied to provide added value to domains such as disease monitoring, healthcare delivery, and services, parallel with assessing the usefulness of such applications from a perspective of meeting the requirements of Saudi Arabia’s population in terms of public health.

Integrating AI in Saudi Arabia’s healthcare system requires addressing several critical research gaps to align with cultural, ethical, and practical needs. Literature often neglects the development of culturally sensitive AI models that respect Saudi Arabian values and Islamic ethics, which is essential for fostering trust and acceptance in AI-driven healthcare solutions. In addition, there is a lack of comprehensive ethical frameworks designed for the Saudi context, including considerations of data confidentiality, algorithmic transparency, and patient autonomy. Empirical studies specific to Saudi Arabia’s healthcare infrastructure and workforce readiness are scarce, with most research focusing on Western contexts, leaving significant gaps in understanding the unique challenges and success factors of AI adoption in the Kingdom. In addition, aligning AI applications with Vision 2030’s objectives, such as enhancing healthcare accessibility, efficiency, and quality, remains underexplored. Another critical issue is the reliance on non-local training data, which limits AI models’ accuracy and relevance for the Saudi population. This review aims to bridge these gaps by proposing culturally sensitive, ethically grounded, and context-specific AI frameworks, offering insights into localized data requirements and strategic alignment with national healthcare goals to support effective AI integration in Saudi Arabia.

The review article primarily aims to investigate three research questions to understand the research progress in this area, including:

RQ1: To what extent and in what ways are AI applications being used for the public healthcare sector in Saudi Arabia?

RO1: To investigate the extent of AI adoption in Saudi Arabia’s public healthcare sector and explore how AI technologies are utilized to enhance public healthcare services.

RQ2: What are the critical areas in public healthcare in Saudi Arabia where AI has been deployed?

RO2: To systematically identify and categorize the critical areas within Saudi Arabia’s public healthcare sector where AI technologies have been deployed, providing insights into AI integration’s leading applications and domains.

RQ3: What future opportunities exist for AI applications that could enhance the sustainability of Saudi Arabia’s public healthcare system by integrating digital technologies?

RO3: To explore and define future opportunities for AI applications that can contribute to building a sustainable public healthcare system in Saudi Arabia, focusing on how digital technologies can enhance these opportunities.

Fig. 1 demonstrates the Review Framework adopted [36,37] for the review article. Sections 2.2 to 2.6 detail the process used for the systematic literature review by the authors, including data selection, keyword selection, acceptance and rejection criteria, and inclusion and exclusion.

Figure 1: Review methodology using PRISMA framework (Adopted from [36,37])

2.2 Systematic Literature Review

A systematic literature review is a meticulous procedure that comprehensively evaluates and analyzes current and past research in a specific domain or region [38]. In addition, it enables the assessment and further exploration of the dominant trends in a specific field of study [39].

Adopting this approach streamlines the process of identifying constraints and possible directions for future investigation. Through evaluating and investigating previous research efforts, this study applies the concepts of SLR to gain a thorough grasp of the previously established research subject [37]. This review used the PRISMA approach for SLR [36].

2.3 Database Selection

The initial stage of starting a literature review is selecting a database to retrieve. The present research includes articles from Emerald Publishing, Taylor & Francis, Elsevier, Springer, and IEEE, which have been included in the SCOPUS and Web of Science databases. The study adheres to the timeline from 2011 till 2024 (August).

2.4 Choosing Keywords

Based on previous literature, the study analyses the practices and strategies for deploying Artificial Intelligence in advancing public health performance in Saudi Arabia using SLR. Appropriate selection of keywords is crucial for curating articles in all fields. For the present study, secondary data is searched using keywords from the Web of Science and Scopus databases. Keywords strings include: “Artificial Intelligence” AND “Public Health” AND “Saudi Arabia”; “Artificial Intelligence” AND “Epidemiology” AND “Saudi Arabia”; “Artificial Intelligence” AND “Biostatistics” AND “Saudi Arabia”; “Artificial Intelligence” AND “Bioinformatics” AND “Saudi Arabia”; “Artificial Intelligence” AND “Environmental Health” AND “Saudi Arabia”; “Artificial Intelligence” AND “Health Services” AND “Saudi Arabia”; “Artificial Intelligence” AND “Community Health” AND “Saudi Arabia”; “Artificial Intelligence” AND “Public Health Ethics”; “Artificial Intelligence” AND “Community Health” AND “Saudi Arabia”; “Artificial Intelligence” AND “global Health” AND “Saudi Arabia”; “Artificial Intelligence” AND “Health Policy and Management” AND “Saudi Arabia”. The search items and systematic literature review carried out in the present research are listed in Table 1.

The search criteria for the study are described below, along with the help of Table 1. This study has made the final selections of the 51 articles to be subjected to final review at the end of the search for articles based on the prescribed systematic literature review across the four-documented process. By the PRISMA approach, the literature review was performed concerning a series of inclusion criteria that reduced the number of analyzed studies in several stages. Table 1 lists the search criteria employed to establish literature on the application of Artificial Intelligence (AI) across different domains of public health in Saudi Arabia. First, a preliminary and general search was made using some keywords linked to AI and its use in the fields of Epidemiology, Biostatistics, Bioinformatics, Environmental Health, Health Services, Community Health, Health and Global Health, Ethics, and Policy in Health and Health Administration and all relevant to Saudi Arabia. An initial search of the articles gave 150 sources to be reviewed. The first step was narrowing down these results to embrace only articles that dealt with Saudi Arabia, bringing the number down to 92. The second screening made the studies more specific by removing conference papers, thus leaving 64 studies on the list. Last, the study utilized a rigorous selection criterion to screen all the identified articles systematically, and only 51 articles were deemed fit for detailed review. These last 51 articles from which the research was conducted serve as the source of the current state of AI applications in public health within Saudi Arabia, which gave the topic a focused and solid groundwork.

2.5 Acceptance and Rejection Criteria

Based on PRISMA methodology, the only items provided are the subjects specified (Business, Management, and Accounting) and the calendar year (2011: 2024). A total of 150 documents were discovered. The ‘Scopus’ and ‘Web of Science’ databases will be queried using every search parameter for future research.

2.6 Inclusion and Exclusion Criteria

English-language inclusion criteria include conferences, peer-reviewed journals, and book chapters. The exclusion criteria encompass articles that have been published in conferences, Non-refereed journals and magazines.

3  Artificial Intelligence Applications

Artificial Intelligence (AI) rapidly transforms public healthcare by improving diagnosis accuracy, personalizing treatment plans, and enhancing operational efficiencies [86]. This review explores AI’s current and potential applications in public healthcare systems, focusing on its impact on diagnostic processes, treatment personalization, patient management, and resource allocation. In addition, it examines the ethical implications, challenges, and prospects of integrating AI into public healthcare, emphasizing the balance between technological advancements and human-centric care [87]. AI transforms public healthcare while advancing diagnostic accuracy, personalized health plans, and operational efficiencies [28]. The review explores existing and future uses of AI, especially in diagnosing and treating diseases, patient care services, and, more importantly, resource allocation in Saudi Arabia’s healthcare sector. In addition, it analyses the ethical concerns, benefits, and disadvantages of AI adaptation to Saudi public health care, focusing mainly on the aspects of technology and patients. Table 2 discusses past research on topics relevant to AI in public healthcare.

3.1 Public Healthcare and AI Applications

With the use of AI in the healthcare industry, the prospects have revolutionized how difficult precipitations were tackled in the past [88]. In diagnostics onwards right up to patient care, artificial intelligence is redefining how it plays in the field of health care professionals and patients [86]. Table 3 illustrates some AI applications in public health systems.

As explained in Table 2, various applications of AI in public Healthcare systems play critical roles. AI, intense learning, enhances diagnostics by analyzing medical images for conditions like tumors and fractures [124]. AI has significantly improved mammography for breast cancer detection, assisting doctors by reviewing patient information and test results [125]. AI accelerates drug discovery by scanning large datasets to determine drug candidates and their effectiveness [62]. AI can also predict patient reactions to new drugs, making the process faster and cheaper [63]. AI helps craft personalized treatments by analyzing a patient’s genetic and behavioral data [126].

It can also ascertain how patients react to the medications [64]. These AI-based platforms engage with patients, schedule appointments, and even offer first-line diagnosis [65]. Robotic systems powered by artificial intelligence make it possible to perform delicate operations with minimal intrusiveness, which results in shorter recovery times [66]. They are also applied in surgical training [127]. Risk assessment is assisted by AI in that it can predict patient readmission rates, progression of diseases, and complications, thus indicating where intervention is needed [68]. It can also predict patient volume in hospitals and deal with available stocks better [68,128]. Automated tasks such as medical coding, billing, and scheduling take much time and require many personnel who can otherwise be dedicated to patient care [70]. Natural language processing further reinforces the quality and utility of electronic health records. In wearable technology, AI will track a patient’s condition and immediately inform the relevant doctor if the patient is in critical condition to prevent severe illness [129]. Nwankwo et al. [130] noted that telehealth platforms provide the needed care through AI, especially for rural areas. It knows that it can diagnose depression and anxiety presence based on patients’ voices [73]. AI also analyses genetic data and contributes to prescribing the proper treatment options, which minimizes cross-attempts [74,75].

3.2 Public Healthcare in Saudi Arabia and AI Applications

In line with RO1, this study examines how Saudi Arabia’s public healthcare sector integrates AI in delivering public healthcare services. AI is now a crucial tool in managing Saudi Arabia’s healthcare challenges. Such AI approaches, such as machine learning, natural language processing, and computer vision, are applied in healthcare and become a reaction to challenges like scarcity of resources, increasing costs, and a need for an individual approach to all processes concerning healthcare [131]. In Saudi Arabia, AI was planned through Vision 2030: The government has revealed plans to achieve intentional AI in the country through Vision 2030, which stresses how AI is vital in engineering superior, more innovative, and more effective service delivery for patients [132]. RQ1 explains how AI is applied in the Saudi Arabian public healthcare sector. Table 4 also represents the indicators of diagnosis, treatment, management, and care using AI applications with instances from Saudi Arabia to substantiate the flexibility of AI in healthcare administrations. Using advanced data analysis, predictive modeling, and real-time data monitoring, AI can effectively enhance disease prediction and management in Saudi Arabia. AI can improve disease prevention in various forms, including (a) Early detection and diagnosis, (b) Personalised treatment, (c) Predictive Epidemiology, (d) Remote Monitoring and Chronic Disease Management, and (e) Resource Allocation and Management. The detailed discussion is as follows:

(a) Early Detection and Diagnosis: AI-enabled algorithms can analyze large amounts of medical data (for instance, patient histories, laboratory results, and others), contributing to early disease diagnosis [133]. For nonsporadic diseases, including diabetes and cardiovascular diseases, AI can point out risk factors and early distributor notice for suitable actions [134]. However, in the Saudi context, when chronic disease becomes dominant, AI can predict diseases and assist in early diagnosis, decreasing overall health costs and improving patients’ health [135].

(b) Personalised Treatment: AI can create a personalized health plan proposed based on the specific EHR of each of the patients [136]. AI models can be implemented to derive and suggest specific procedures for numerous chronic diseases. The patient develops plans based on their requirements, thus enhancing the recovery periods and health management [137].

(c) Predictive Epidemiology: AI-assisted analysis can be used for epidemiologic data to predict disease occurrences and events of large assemblies in tracking disease spread [138]. Healthcare planners in Saudi Arabia can use AI-led predictive models to forecast infectious disease trends [15]. It can easily result in accurate targeted vaccination, resource designation, and well-timed public health measures undertaken. AI can analyze wearable devices or mobile health applications to monitor health status remotely. These aids enable early interventions if some errors are detected [139]. AI can revolutionize Saudi Arabia’s healthcare sector regarding resource allocation, genomic studies, and mental health analytics [140]. Using analysis of large patient data and forecasts of future inflow of patients and resource use, coupled with worker productivity, AI is capable of optimizing healthcare delivery, especially in high population mobility events such as stigmatization whereby the spiritual event of Hajj poses a great challenge in disease control and management as well as emergency preparedness [141]. In addition, AI’s efficiency in analyzing genetics data plays a vital role in defining the risk factors for hereditary diseases caused by marriage within Saudi Arabia, which is the term for early diagnosis through genetic testing programs [142]. In mental health, AI-powered tools can detect patterns associated with mental health conditions using patient interactions, digital health platform responses, and voice or text analysis, fostering early detection and effective support [143]. In addition, AI-driven analysis of population-level health data from electronic health records reveals trends, risk factors, and outcomes, guiding public health planning and initiatives, particularly for non-communicable diseases [144]. Collectively, these applications contribute to more efficient disease management, improved healthcare quality, and better health outcomes across the population [145].

This table and discussion directly address RQ1 by examining the extent and diverse ways AI applications are being implemented to enhance healthcare services in Saudi Arabia, driving the nation’s public healthcare system towards its Vision 2030 objectives.

3.3 Potential Barriers to AI Implementation in Saudi Arabia’s Public Health System

Adopting AI in Saudi Arabia leads to innovative and sustainable transformation in the public healthcare system [172]. However, this transformation is witnessing various barriers that must promptly be addressed to ensure integrated development and societal acceptance [173]. The critical barriers include data privacy and security, workforce management, high implementation costs, cultural concerns related to AI adoption, and interoperability with existing healthcare infrastructure [174,175]. Addressing these challenges requires a strategic integrated approach. The approach helps improve the digital infrastructure to support a sustainable AI ecosystem in the healthcare domain. Overcoming these barriers enables Saudi Arabia to harness the potential of AI in public health in alignment with Saudi Arabian Vision 2030. The potential Barriers to AI implementation in the Saudi Arabian Public Healthcare System include (a) Data Privacy and Security Concerns, (b) Lack of Skilled Manpower, (c) Cost of Implementation, (d) Change Resistance, (e) Interoperationability, (f) Regulatory and Ethical Challenges, (g) Algorithmic Bias and Local Relevance, and (h) Infrastructure and Technology Gaps.’ The detailed description is as follows:

(a) Data Privacy and Security Concerns: Misuse of health information can influence patient trust [176]. Strategically, Saudi Arabia should consider implementing privacy-preserving AI techniques like federated learning, which allows AI models to be trained on decentralized data without centralizing sensitive information and fortifying privacy laws [177]. This method reassures patients about the security of their data while improving privacy and compliance with data protection laws.

(b) Lack of Skilled Manpower: Deploying AI in healthcare needs employees who lack expertise in data analytics, AI, and healthcare application skills, which Saudi Arabia currently lacks [178]. Along with training initiatives, Saudi Healthcare institutions can establish academic tracks focused on AI to encourage the growth of a regional talent pool [132]. The government might also create partnerships with global AI research institutions to promote exchange programs, knowledge sharing, and collaborative research initiatives and provide career development grants as incentives for healthcare professionals to specialize in AI [179].

(c) Cost of Implementation: Some healthcare facilities might discover the initial setup costs of AI exorbitant, especially for smaller facilities [180]. The government can establish a fund that focuses on the use of AI in healthcare, offering preference to initiatives that assist in accomplishing Vision 2030 targets, such as preventive care and managing chronic illnesses [181]. In addition, healthcare organizations can see immediate advantages without experiencing significant financial burdens by using AI modularly, starting with smaller, scalable AI systems [182].

(d) Change Resistance: Patients’ and healthcare providers’ unwillingness to use AI technology can result from concerns about replacing human workers and their lack of familiarity [183]. This fear can be alleviated by establishing change management initiatives and displaying case studies demonstrating AI’s beneficial effect in healthcare. Establishing “AI Champions” in healthcare organizations, reliable people with AI training who advocate its advantages can help improve colleagues’ and patients’ views of AI [184].

(e) Inter-operationability: Legacy systems have limited data sharing and integration capabilities [185]. Along with adopting standardized data protocols, Saudi Arabia can emphasize investments in middleware solutions that facilitate data transfer from legacy systems to AI-enabled platforms [186]. Another way to guarantee seamless integration is to form alliances with technology service providers to explore interoperability as a design element [187].

(f) Regulatory and Ethical Challenges: Without regulatory guidelines specific to AI in healthcare, healthcare Supply Chain partners often face uncertainty around AI applications [188]. Establishing a regulatory body of various healthcare stakeholders aims to streamline the development and deployment of AI-specific healthcare regulations to develop comprehensive, culturally aligned AI guidelines.

(g) Algorithmic Bias and Local Relevance: AI models trained on non-regional data are likely unsuccessful due to unique demographical and health-related patterns in Saudi Arabia that can lead to biased results [189]. To address this barrier, policymakers can develop guidelines encouraging AI developers to conduct bias audits and fairness testing for AI models [190]. Another strategy can be to conduct collaborative research with Saudi Arabian health institutions, as the research partnership can improve AI models’ accuracy, fairness, and relevance [191].

(h) Infrastructure and Technology Gaps: Digital infrastructure, such as high-speed internet services and storage capabilities, is an elementary requirement for AI implementation. This challenge is catered to by expanding the existing digital infrastructure for healthcare AI [8]. Thus, strategic solutions can help Saudi Arabia address AI implementation challenges while creating a supportive environment for AI in healthcare [192]. By strategically coping with these implementation barriers, Saudi Arabia can position itself as a leader in culturally sensitive, secure, and effective AI-driven healthcare innovation to support Saudi Arabian Vision 2030. Fig. 2 demonstrate the potential barriers to AI implementation in Saudi Arabia.

Figure 2: Demonstrates the potential barriers to AI implementation in Saudi Arabia

4  Critical Success Factors (CSFs) of AI Integration in Public Healthcare in Saudi Arabia

4.1 Critical Areas of AI Deployment in Public Healthcare

In alignment with RO2, which aims to systematically identify and categorize the critical areas within Saudi Arabia’s public healthcare sector where AI technologies have been deployed, several key domains have emerged as focal points for AI integration. These key areas illustrate AI’s transformative potential and provide insights into the leading applications in Saudi healthcare [193]. As RQ2 aims to identify these deployment areas, the following section identifies key challenges, opportunities, and Critical Success Factors (CSFs) that affect AI applications in the Kingdom of Saudi Arabia’s public healthcare facilities. AI proves to be an essential element in improving public healthcare services at different levels. However, its integration is conditioned to some extent by CSFs that are technological, ethical, organizational, and regulatory [194]. Implementing these analytics in Saudi Arabia is critical to addressing cultural, social, and infrastructural peculiarities [173]. CSFs that span technological, ethical, organizational, and regulatory challenges [195]. These CSFs are especially important when addressing cultural, social, and infrastructural contexts unique to Saudi Arabia [83]. Table 5 illustrates Critical Success Factors (CSFs) and their impact from the perspective of Saudi Arabia’s AI integration into public healthcare. The critical areas for AI deployment (in Fig. 3), along with their respective challenges, include:

i) Data Privacy and Security: As AI technologies involve massive datasets for operations emphasizing health sectors, data protection and security remain aspects of significant value [196]. In Saudi Arabia, AI in healthcare means processing patient data and, therefore, must follow the PDPL. Following these standards is necessary to maintain patients’ trust and safeguard their personal information because privacy is susceptible in Saudi Arabia. AI solutions must also consider a data breach and a hostile takeover of healthcare systems [197].

ii) Algorithmic Bias and Fairness: If trained on inadequate or limited datasets, AI decision-making systems create discriminative models, resulting in discriminating patient treatment. This issue especially is arousing in Saudi Arabia since the country is ethnically mixed, with citizens and a strong presence of immigrant workers. Large-scale disparities in the ability and opportunity to receive healthcare and treatments can worsen if some AI algorithms are programmed to perform better with specific population segments [198]. For accuracy and non-discrimination, AI systems should be put back with approaches that reduce prejudice and satisfy each cohort’s demand for health care [199].

iii) Lack of Interoperability: The structure of both the public and private sectors of healthcare organizations in Saudi Arabia is presently relatively decentralized, and the implementation of numerous EHR platforms and systems differ from each other in their data representation. A lack of integration between systems causes many problems that hinder AI integration and prevent the effective exchange of information and integration between healthcare entities [200]. The necessity for increased AI effectiveness in public healthcare means that standardizing the IT structure and the type of data involved is essential [51].

iv) Regulatory and Legal Challenges: There is still an emerging set of rules relating to the use of AI in Saudi Arabia’s healthcare sector [92]. There are initiatives to develop guidelines that embrace Vision 2030 of the Kingdom of Saudi Arabia. However, the emerging issues include liability of the AI outward form, patient consent, and accountability in healthcare AI decisions [201]. There have to be policies that will encourage innovation in the best interest of the patients; there have to be measures regarding how the ethical and legal standards must be followed:

v) Cultural Sensitivity and Ethical Concerns: In Saudi Arabia, AI-enabled technological usage in products and services can align with cultural sensitivities and ethical considerations [178]. During the design phase, Islamic ethical principles (including patient privacy, autonomy, and dignity) can be incorporated through the engagement of local healthcare professionals [202]. The AI models are built to alert users about privacy policies in a culturally accepted manner to maintain Islamic patients’ autonomy [203]. In addition, developing AI interfaces through digital assistance and chatbots in modern standard Arabic and regional dialects can ensure effective human-medical device interfaces while adapting local idiomatic expressions and culturally viable health communication language [204]. In addition, an Islamic ethical framework can be developed to cover guidelines related to data privacy, consent, transparency, and patient welfare in AI applications [205]. The ethical framework should align with regulations specific to AI in healthcare to supervise sensitive data related to data privacy, gender sensitivity, and public trust [202]. Using epidemiological Saudi-specific health data to train AI models can enhance disease prediction accuracy. Also, it improves the reliability of AI models for local health needs [206]. In the context of Saudi cultural norms, AI-enabled telemedicine platforms can offer customized gender-specific interfaces and solutions to ensure seamless experiences [207]. Community engagement through workshops and outreach sessions educates the public about AI benefits in healthcare in Saudi Arabia [208]. In addition, AI-enabled hospitals should undergo monthly, biannual, and annual reviews, where patients’ feedback and cultural experts’ comments and feedback can help identify, assess, and contribute to developing and restructuring cultural values and ethical guidelines [209].

These cultural and ethical issues resulting from the adoption of AI in healthcare include whether a patient has permission to be treated using an AI model, how much information should be provided to the patient, and how much human intervention should be allowed during the use of AI models [210]. These concerns are further magnified in Saudi Arabia by the Islamic medical culture of ethical norms and protocols rooted in patient rights, self-respect, and dignity [211]. AI applications must conform to these cultural and religious sentiments to be acceptable across the globe; at the same time, healthcare decisions must be made wisely and with a visible ethical backbone [212].

vi) Infrastructure and Workforce Readiness: AI deployment in Saudi Arabia’s public healthcare delivery also requires the proper infrastructure and a competent human resource. It can be understood that AI applications in healthcare now depend on high-performance computing spaces and both advanced networking and storage facilities. In addition, investment in the education and training of healthcare professionals in using AI tools is insufficient, which poses a significant challenge for enhancing the use of AI tools [213]. Though successive Kingdom governments have sought to develop the country’s digital infrastructure, more infrastructure and human capital are needed to advance AI adoption [214].

vii) Trust and Acceptance: Healthcare professionals and patients must trust AI technologies to enhance adoption. Accordingly, based on RQ2, Saudi Arabian culture and religion play an important and extensive role in perceiving AI in healthcare. They should isolate themselves culturally and demonstrate how they can enhance patients’ comfort without negating the Saudi culture to make the principles of AI acceptable for adoption [215]. It is high time to increase people’s awareness and understanding of AI drivers and constraints through obligatory public education and clear and persistent information sharing [216]. Table 5 illustrates the Critical Success Factors (CSFs) and their impact from the perspective of Saudi Arabia’s AI integration into public healthcare.

4.2 Strategic Action Plan for CSF Implementation

In order to effectively address the implementation of CSFs of AI in Saudi Arabia’s public healthcare system, a strategic action plan can be developed focusing on the critical areas including:

i. Strengthening Data Privacy and Security: It is necessary to adopt and apply strict policies that protect the received data based on HIPAA and other USA and UK legislation requirements to protect the collected and stored information [239]. Strengthening data privacy and security involves investing in the tools to achieve cybersecurity and applying the latest features, such as encryption methods, second-factor authentication, and security scanning. From the healthcare perspective of Saudi Arabia, it preserves the security of patients’ health information and patients’ identity to build confidence [240].

ii. Mitigating Bias in AI Models: The action plan targets reducing Biased AI by employing multiple data sources to train AI models, making the AI use balanced [241]. This will be undertaken by working with global and national bodies to collect structured information to capture the diversity of people in the Kingdom of Saudi Arabia. Validation and testing techniques will also be applied to diagnose and eliminate bias where necessary. Therefore, the expected outcome is reducing healthcare disparities. AI models should provide equally good solutions for people of all categories [242].

iii. Navigating Regulatory Approvals: In executing this plan to advance the growth and use of AI in healthcare, the plan will include setting up a regulatory agenda specific to Saudi Arabia that involves working with the regulatory authorities, those in the sector, and AI solution providers to create a more efficient approval process and coming up with set standard in guidelines that will foster AI innovation without jeopardizing patient safety or ethical issues [243,244].

iv. Facilitating Integration with Existing Healthcare Systems: As a result of the sequential implementation, there shall be a planned approach to the stages at which the application of AI tools will occur, then pilot implementation at hospitals, training of the working professionals and provision of the resources required for such change without compromising on efficiency, workflow or even patient care for the improvement of health care delivery systems [245].

v. Cooperation and Disruptive Thinking: It means supporting partnerships between the public and private sectors and, more importantly, grabbing international relationships to advance the use of AI in healthcare [179]. This can be undertaken by developing innovation zones in health care and ensuring the provision of incentives to undertake research and development in the areas, including grants and tax credits to attract the right talent and capital [246]. The goal of AI adoption is to advance Saudi Arabian healthcare to be at par with other countries’ AI-driven health systems for the improvement of public healthcare and the economy’s growth [79].

vi. Enhancing Public Awareness and Trust in AI: Public awareness campaigns are critical in enhancing the public understanding and awareness of AI in healthcare [247]. The awareness campaigns are mainly that of health promotion, safety of AI and applying uses of AI in the public care system, communication through social media, and the promotion concerning the usage of AI applications in the public care context [248,249].

vii. Establishing Innovation Zones: Saudi Arabia should also develop innovation zones or centers to enhance the application of AI in health care and promote its use [192]. Most of these hubs will focus on conducting research, development, public-private partnerships, and international links. Offering grants and tax credits encourages talent attraction and investments, making Saudi Arabia the preferred hub for AI healthcare [84].

viii. Monitoring and Evaluation of AI Implementation: Benchmarking is necessary to control and assess the application of AI in healthcare and its success [139]. This entails utilizing the already set key performance indicators (KPIs) that will be employed to determine the extent of implementation, the result, and the outcome achieved to identify areas for adjustments. AI implementation review cycles must be set up to achieve the desired objectives without negative implications to patient safety or ethics [250]. Ongoing assessment will enable the development of relevant changing strategies to keep the integration of artificial intelligence pertinent to the current needs of the healthcare system [251]. Relating to the measures that can be taken to ensure public awareness and acceptance of their use in health care, there is a need to launch public awareness campaigns for health literacy. This means launching public awareness campaigns on the safety and possibility of using artificial intelligence in the health care system.

This can be done through social media to post success stories, sometimes address concerns, and post information regarding AI’s application in healthcare. In addition, community leaders and healthcare professionals should be encouraged to support AI development. The idea is to create commonly recognized trust in AI technologies to achieve support for their application. Fig. 4 demonstrates the Strategic Action Plan for CSF implementation. Table 6 lists the Strategic Action Plan, Implementation Steps, and Outcomes.

Figure 4: Strategic action plan

Table 5 details how addressing these strategic areas can help Saudi Arabia’s public healthcare system effectively overcome the challenges of implementing AI, leading to improved healthcare outcomes and enhanced system efficiency.

4.3 Content Analysis for Themes Identified

In line with RO3, which seeks to explore and define future opportunities for AI applications to enhance the sustainability of Saudi Arabia’s public healthcare system, this section focuses on integrating digital technologies and their potential to drive these opportunities forward. The study identified key research themes related to the future deployment of AI in healthcare utilizing thematic analysis, highlighting the most promising areas for innovation and sustainability and addressing RQ3. The systematic literature review and thematic analysis conducted in R Studio reveal multiple themes based on their density (degree of development) and centrality (relevance), presented in a 2 × 2 matrix.

Fig. 5 depicts a thematic map that categorizes these research themes into four quadrants: Motor Themes, Niche Themes, Emerging or Declining Themes, and Basic Themes. Each quadrant represents different research clusters that guide the future development of AI applications in Saudi Arabia’s public healthcare system, contributing to sustainability efforts.

Figure 5: Thematic analysis

The analysis points to numerous future opportunities for AI-driven sustainability in Saudi Arabia’s public healthcare system by addressing RQ3 and focusing on these identified themes. AI’s integration with digital technologies, including predictive analytics, mHealth, and decision support systems, promises to enhance the system’s overall efficiency, quality of care, and ability to meet public health demands, aligning with RO3’s goal of promoting sustainable development through technology.

Theme 1: Artificial Intelligence-Human factors for sustainable public Health services in Saudi Arabia.

Fig. 5 indicates that Motor Themes are located in the upper-right quadrant, showing high density and centrality, meaning that they are widely used and essential to the field of study. This quadrant includes concepts like ‘artificial intelligence,’ ‘human factors,’ and ‘Saudi Arabia.’ Not only are these themes well-emerged, but they are also central to advancing knowledge in the overall field of study. The relevance of such themes is evidenced by their prominence and significance to public health care, where the adoption of AI is viewed as disruptive. The positioning of ‘Saudi Arabia’ in this quadrant also reinforces its appropriateness because the region is leading in developing AI health solutions. The interrelation between Theme 1 and Proposition 1 is shown in Fig. 7.

Figure 6: Various themes on public health services in Saudi Arabia

Figure 7: Theme 1 and Proposition 1

Proposition 1: Evaluation of the inter-relationship between Artificial Intelligence-Human factors for sustainable public health services.

Theme 2: AI-Enhanced Quality Control: Elevating Standards and Efficiency in Saudi Arabia.

This is classified as niche themes, which are highly developed but not so central and are addressed in the upper-left quadrant. Topics like models, quality control, air quality, communicable diseases, health services, and mental health are included in this category. Although these issues do not set a tendency that is popular and embraced throughout the field, they deal with specific areas of study and are highly complex and, in many cases, detailed. These themes probably serve interest-specific, specialized research specialists and can provide detailed, innovative methods or trends within a narrow sub-discipline.

Proposition 2: Investigation of the potential of AI in improving quality assurance in Saudi Arabia’s public healthcare systems and formulate operational, technical, and legal Strategies for its sustainable performance.

The interrelation between Theme 2 and Proposition 2 is shown in Fig. 8.

Figure 8: Theme 2 and Proposition 2

Theme 3: Emerging Technological and Environmental Frontiers in Public Healthcare System.

Water Resources, mHealth, Predictive Analytics, Learning Algorithms, IoT, and Hypertension. Low density and centrality characterize emerging or declining themes in the lower-left quadrant. This Cluster consists of themes: Water Resources, mHealth, Predictive Analytics, Learning Algorithms, Internet of Things (IoT), and Hypertension. These issues can be interdisciplinary fields that are still in the process of establishing the kind of acceptance within the scholarly world and academic circles, or they can be dwindling or stagnant disciplines. Both points indicate that the analyzed themes are still underdeveloped and do not occupy a central place in research. Thus, they can be classified as topics that are either in the process of evolving and, therefore, still being explored by researchers or gradually losing their significance in the context of the scientific investigation.

Proposition 3: To investigate and implement the use of emerging technologies such as Mobile health, predictive analysis, learning algorithms, Internet of things (IoT), and environmental health system in Saudi Arabia, emphasizing the resource-scarce water supply and disease prevention, health facility access, and management of chronic diseases like hypertension.

The interrelation between Theme 3 and Proposition 3 is shown in Fig. 9.

Figure 9: Theme 3 and Proposition 3

Theme 4: Advanced Decision-Making and Technological Integration in Middle Eastern Healthcare Systems. The identified cluster is Basic Themes: The lower right is divided into Basic Themes, and while being less dense, they are focused on the research domain. Some topics that can fall under this category include Decision Making, Decision Support Systems, Hospital Management, Epidemiology, Forecasting, Algorithms, Machine Learning, Healthcare Personnel, Middle East and all other related topics. These generic themes are part of the field’s core, and the subsequent more specific and complex research is based on these themes. Despite being less developed, their importance suggests that these are key research directions that can provide a base for increasingly sophisticated and interdisciplinary investigations.

Fig. 6 explains Various themes of Public Health services in Saudi Arabia. Comparing AI implementation in healthcare indicates the situation in Saudi Arabia and prospects in the Middle East and globally, as well as its strengths and future improvements. They have constraints similar to those in Saudi Arabia, such as data privacy, cultural integration, and a lack of AI talent, but they have a better AI-rooted environment than the UAE. The Global leaders are the United States and South Korea in AI diagnostic solutions, pharmacogenomics, and digital health supported by well-established regulatory legislation and public-private partnerships. These nations highlight the importance of effective data management and ethical-issue AI, the aspects Saudi Arabia is developing under Vision 2030. Saudi Arabia has been credited with handling unique demands, such as managing mega-demands such as the Hajj event. The issues connected with the development of the healthcare workforce, localized research, and data creation are still present to a greater or lesser extent. However, by strengthening international partnerships and enhancing the regulatory environment, Saudi Arabia has all the potential to become one of the leaders in a culturally appropriate, AI-based healthcare system in the Middle East and beyond.

Proposition 4: To explore and model decision support systems (DSS), machine learning, and predictive analytical solutions that would affect the management of hospitals, the epidemiology of diseases, and the effectiveness of the healthcare personnel in countries of the Middle East with a purpose of solving Middle East’s healthcare problems and improving the general performance of the healthcare systems.

The interrelation between Theme 4 and Proposition 4 is shown in Fig. 10. Integrating sustainable and culturally sensitive AI in Saudi Arabia’s healthcare system can significantly enhance the quality of care while aligning with Vision 2030 goals. Sustainable AI practices reduce environmental impact, conserve resources, and promote equitable healthcare, emphasizing energy-efficient models and eco-friendly operations to minimize the carbon footprint. Environmental factors like extreme heat and high foot traffic during events such as Hajj necessitate adaptive AI deployment, with region-specific solutions to ensure functionality under diverse conditions. Ensuring quality control through robust testing, validation, and updates is critical for AI reliability in diagnostic imaging and predictive modeling applications. Culturally and ethically sensitive AI integration, aligned with societal and religious values, fosters trust and public acceptance, requiring privacy protection, consent management, and adapted algorithms. In addition, AI’s role in disease prediction and management can revolutionize chronic care through predictive analytics and personalized treatment, improving patient outcomes and healthcare accessibility. These insights provide actionable recommendations for healthcare providers and policymakers to adopt green AI technologies, develop quality assurance frameworks, and implement culturally aligned, patient-centric AI solutions to enhance healthcare sustainability and effectiveness in Saudi Arabia.

Figure 10: Theme 4 and Proposition 4

5  Discussion

Artificial Intelligence (AI) is transforming healthcare as a disruptive force, offering novel ways to enhance diagnostics, streamline administrative operations, and tailor therapeutic strategies [275]. In Saudi Arabia, AI has started its journey in harmony with the Kingdom’s Vision 2030 Main Goals, technological development and better healthcare services. Thus, fundamental and practical issues are linked to the use of AI in healthcare. For AI systems to reach their full use, they must be updated often, meaning addressing both technical and human elements that determine AI impact [203]. Accordingly, the qualitative thematic analysis carried out in this study reveals four areas where AI can make a significant contribution to sustainability in Saudi Arabia’s public healthcare. First, integrating AI into the existing systems must involve collaborating with human beings in the health sector. This synergy intervention will be imperative for the practical realization of long-term goals in public health since AI can boost services. Still, human factors are vital in service delivery. Second, applying AI to quality assurance is another area where the technology can gain organizational, technical, and legal advancements, especially in disease transmission prevention and mental health intervention services. Third, future technologies, including mHealth, predictive analytics, and IoT, will probably provide insights into the scarcity of resources and chronic diseases and substantive approaches to a sustainable health system. Accordingly, this context indicates that such tools and models as decision support systems (DSS) and machine learning can complement the management, epidemiology, and healthcare workforce delivery in hospitals in Saudi Arabia and design the platform for further studies of the future complex roles of AI in the Kingdom’s healthcare sector and beyond.

The research suggests specific measures to ensure the privacy and security of patient data when using AI in healthcare. Table 7 demonstrates specific measures to ensure privacy and security of patient data using AI in Healthcare.

Ensuring the privacy and security of patient data in AI-powered healthcare, particularly in Saudi Arabia’s sensitive public healthcare sector, requires comprehensive measures. The Saudi Health Information Exchange (SHIE) policies must be followed, and regular security assessments must be conducted to identify potential breaches of GDPR. Eliminating or limiting the amassment of data and using federative learning or edge computing reduce exposure to centralized processes. The healthcare staff should be trained on privacy issues, data management policies, and decision-making related to patient trust. In alignment, these strategies enable Saudi Arabia’s healthcare system to use AI effectively while risking patient privacy and creating a safe and reliable healthcare atmosphere. The other implication of the findings of this research is the policy implications and usage of AI in catering to mass gatherings and delivering public healthcare. AI has a tremendous possibility to transform the availability of health care services and their results during such an event as Hajj; therefore, millions exert pressure on the health care system in the process [298]. Table 8 depicts the various AI applications which are implemented in public healthcare and patient care during mass gatherings.

AI technologies can provide innovative systems to address crowd health amidst gatherings such as Hajj by providing better healthcare, thus supporting Vision 2030. Predictive modeling includes processing past and present claims and other relevant information to predict future healthcare demands, like the possible occurrence of an epidemic or exploration of risks and preparedness for their realization to optimize resource allocation. AI-terminals and cameras observe crowd density in real-time and identify early signs of illness; wearables monitor stars and raise alarms to healthcare teams due to symptoms, such as heat stress or first signs of restricted breathing. AI also helps reduce response time in an emergency through triage that separates severe conditions and predicts when medical equipment and personnel will be needed. Data analysis in contact tracing contains an outbreak, movement data to prevent disease spread, and language translation for non-English-speaking pilgrims. Telemedicine applications based on artificial intelligence enable remote consultations, which help relieve stress for onsite workers, while environmental measuring systems evaluate threats associated with air quality or temperature and then contribute to taking preventive actions. AI processes aggregated data to identify potential public health risks to enhance readiness during subsequent events.

AI is central in actualizing Saudi Arabia’s Vision 2030 goals, and enhancing the availability, quality, and uptake of improved technology is significant to Saudi Arabia’s Vision 2030 work plans, mainly information to the healthcare sector, its availability, quality, and use of advanced technology. Specifically, predicting the healthcare demand makes work more efficient, along with the proper use of infrastructures; thus, it contributes to the sustainable growth of healthcare outsourcing. Increasing the efficiency of drug discovery, genomics, and individualized treatment paves the way for enhancing new medical research and development hubs in Saudi Arabia. AI ensures patient satisfaction by paying individual attention to their condition and ensuring that they receive their health administrative goals. It ensures that administrative goals are achieved and served through surveillance during phases such as Hajj. It also promotes the financial stability of healthcare since jobs performed get done automatically, on diagnoses, there are high chances of getting it right, and operational costs are overcome. In addition, training the employees’ human capital through AI will be knowledge-based in the transformation agenda of Vision 2030. The aid of AI enhances relevant decision-making and policymaking in Saudi culture. Table 9 discusses healthcare applications that enhance responsiveness and improve innovative health systems in light of Vision 2030.

Policymakers and healthcare managers in Saudi Arabia foster an environment that encourages the adoption and innovation of AI in healthcare. Establishing a robust foundation for AI in Saudi Arabia’s healthcare system requires a multi-faceted approach involving clear regulatory frameworks, prioritized data privacy, and strong public-private partnerships (PPPs). Comprehensive guidelines addressing ethical considerations, patient safety, and data protection aligned with standards such as SHIE and GDPR will reduce uncertainty and build stakeholder trust. Promoting PPPs and international collaborations will accelerate innovation through shared expertise and resources. Dedicated AI training programs and education initiatives will create a skilled workforce capable of leveraging AI effectively. Establishing innovation hubs and research centers will foster cutting-edge development tailored to Saudi Arabia’s healthcare needs, while financial incentives, such as grants and subsidies, will lower barriers to adoption. AI ethics committees will ensure cultural alignment and uphold patient rights while focusing on digitizing and standardizing health data, which will improve AI accuracy and relevance. Pilot projects can demonstrate AI’s value in diverse healthcare settings, encouraging widespread adoption. Public awareness campaigns will engage and educate citizens, fostering acceptance and trust in AI technologies. Dedicated funding for AI research in areas such as chronic disease management and resource optimization will drive innovation, and fostering collaborative ecosystems among healthcare providers, startups, academia, and government will accelerate the integration of AI solutions into Saudi Arabia’s healthcare system.

The research gives broad implications of AI for healthcare workforce development and training in Saudi Arabia. AI integration in the assessment of health services in Saudi Arabia is transforming the context of workforce promotion and preparing. It affects the competencies care workers should possess and the structural changes that healthcare education requires. Health workers need to become knowledgeable in the concepts, tools, and processes surrounding AI to work effectively with AI systems; these include specific programs on AI basics to interpret results and decision-making under AI augmentation. AI implementation leads to many new job avenues, such as clinical data analysts, AI ethicists, and healthcare tech, which provides educational institutions with Health informatics and digital certifications. Machine learning and predictive analytics have become part of medical programs and offer requisite courses to sharpen the future workforce alongside mandatory continuing education programs for the current workforce. The focus on data protection, ethical deployment of AI, and constructing critical thinking and interdisciplinary teamwork will significantly add to readiness. Experience with virtual health communications or limited and thus learning focused on interacting with both AI tools and, ultimately, patients through simulation-based means will prove advantageous. Further, breaking tedious administrative work will allow Health service providers to focus more on patient-centered care. Thus, promoting an AI culture aligned with Saudi Arabia’s Vision 2030 confirms sustainable and efficient healthcare services.

6  Future Research Directions

A large-scale research agenda on integrating AI into Saudi Arabia’s healthcare system can help fill the cultural nuances of AI adherence and outcome research. Future empirical studies can examine how AI applications in Saudi Arabia are compatible with Saudi culture and Islamic belief, introducing practical recommendations from qualitative interviews or surveys based on privacy protection, gender, and cultural sensitivity. Self-administered, cross-sectional surveys and interviews with healthcare workers would ascertain factors affecting AI acceptance, including work pressures, ethical considerations, and patients’ consequences for acceptance of AI by developing protocols for training specifically to help healthcare workers match their willingness to accept help from AI with their conceptual model of how patients and healthcare should work. Surveys measuring patient attitudes towards AI-based healthcare solutions can identify aspects that build trust, thus making AI systems culturally palatable. Longitudinal research that chronologically captures the trends of implementation of AI in the healthcare regime, the outcome modifier, or the patients’ benefactor would help to establish solid empirical evidence for the steady use of AI. Exploratory qualitative case studies using AI-based health programs during hajj can provide broadly applicable culturally appropriate solutions for crowd health in high-risk environments. Collectively, these studies would develop a body of knowledge needed for culturally appropriate and sustainable integration of AI in the kingdom’s health care sector. Table 10 explains the research thrust areas and methodology of future studies.

The research directions offer a balanced approach that seems to call for additional focused empirical research addressing the issues arising from culture and the practicality of the theories.

Tools and models such as decision support systems (DSS) and machine learning can complement the management, epidemiology, and healthcare workforce delivery in hospitals in Saudi Arabia and design the platform for further studies of the future complex roles of AI in the Kingdom’s healthcare sector and beyond. Finally, investing in technology and human capital is vital. This includes upgrading technological platforms and ensuring that healthcare practitioners are well-trained in AI applications. AI can enhance the efficiency, effectiveness, and equity of Saudi Arabia’s public healthcare system, ultimately contributing to the nation’s goal of achieving Vision 2030.

The government should adopt a policy on AI good practices that encourage the ethical and culturally appropriate, promotion of AI policies that focus on data protection, user consent, and algorithmization and execution regulation. The healthcare sector should have a separate AI governance committee that will maintain compliance with the above policies in relation to Islamic values and Vision 2030. Specifically, AI education and training for healthcare professionals should be performed to improve AI literacy, practical aspects of AI, and ethical concerns that allow AI to be easily integrated into a working environment. There are ways to finance the inclusion of AI to extend and improve these services to areas where facilities and qualified staff are scarce. Incentives and subsidies will play the most important part in getting there. Governments and private sectors should collaborate to co-design AI solutions suitable for Saudi Arabia’s environment. Promising sectors, such as smart healthcare and disease prognosis, should advance rapidly. Culture and ethics-specific solutions created with the help of cultural and religious advisors integrate Islamic ethics and form the base for AI solutions that the public will trust. Pilot projects for AI solutions such as diagnostics and telemedicine, combined with the availability of massive data for future application to serve public health surveillance and mass gatherings like Hajj, will position Saudi Arabia as a pioneer in adopting AI in the healthcare sector, including resource allocation, health risk monitoring in real-time and disease prevention.

Table 11 lists the policy recommendations and future implications for healthcare practitioners and policymakers to have a clear pathway for adopting AI that aligns with Vision 2030 [299,300]. These strategies will help Saudi Arabia achieve a sustainable, culturally responsive, and technologically advanced healthcare system.

7  Conclusion

AI has started its journey harmoniously in Saudi Arabia with Vision 2030’s primary goals: technological development and better healthcare services. Thus, fundamental and practical issues are linked to the use of AI in healthcare. For AI systems to reach their full use, they must be updated often, meaning addressing both technical and human elements that determine AI impact. Accordingly, the qualitative thematic analysis conducted in this study reveals four areas where AI can significantly contribute to sustainability in Saudi Arabia’s public healthcare. First, integrating AI into the existing systems must involve collaborating with human beings in the health sector. This synergy intervention will be imperative for the practical realization of long-term goals in public health since AI can boost services. However, human factors are vital in service delivery. Second, applying AI to quality assurance is another area where the technology can gain organizational, technical, and legal advancements, especially in disease transmission prevention and mental health intervention services. Third, future technologies, including mHealth, predictive analytics, and IoT, will probably provide insights into the scarcity of resources and chronic diseases and substantive approaches to a sustainable health system. Developing AI frameworks to suit Saudi Arabia’s cultural, ethical, and healthcare needs can revolutionize the sector, aligning with Vision 2030’s goals. Culturally-aware AI ensures adoption and public trust by respecting societal values in data collection, patient interactions, and ethical applications. AI-driven predictive models enable early detection and preventive care for chronic diseases, easing the burden on healthcare systems. Integrating genomics with AI fosters personalized medicine, offering targeted therapies for hereditary conditions common in the region. Fourth, Advanced AI tools for telemedicine and remote monitoring improve healthcare accessibility in underserved areas, while epidemic prediction models enhance public health safety during mass events like Hajj. Decision support systems empower clinicians with accurate diagnostics and treatment recommendations, while multilingual NLP bridges language gaps, enriching patient-provider communication. AI optimizes resource allocation, streamlining supply chains and staff scheduling for greater efficiency. Mental health support systems use AI to provide early interventions and monitor high-risk individuals. Robust data privacy frameworks and explainable AI models build trust and transparency, ensuring patient confidentiality and confidence in AI-assisted care. AI accelerates drug discovery, offers personalized treatment protocols, and monitors environmental health impacts, addressing climate-related health risks.

Collaborative research networks drive innovation and skill development, establishing a self-sustaining AI ecosystem tailored to Saudi healthcare needs. Investing in these key areas of AI research and development can create a more efficient, responsive, and accessible healthcare system in Saudi Arabia. These advancements will support the Kingdom’s Vision 2030 goals, positioning Saudi Arabia as a leader in AI-driven healthcare transformation and sustainable public health innovation.

Acknowledgement: None.

Funding Statement: This research has been funded by the Scientific Research Deanship at the University of Ha’il-Saudi Arabia through project number-RG-23 251.

Author Contributions: The authors confirm contribution to the paper as follows: study conception and design: Rakesh Kumar, Sudhanshu Joshi, Manu Sharma; data collection: Sudhanshu Joshi, Manu Sharma, Ajay Singh, Mohammed Ismail Humaida; analysis and interpretation of results: Rakesh Kumar, Ajay Singh, Sudhanshu Joshi, Manu Sharma; draft manuscript preparation: Rakesh Kumar, Ajay Singh, Sudhanshu Joshi, Manu Sharma, Ahmed Subahi Ahmed Kassar, Mohammed Ismail Humaida. All authors reviewed the results and approved the final version of the manuscript.

Availability of Data and Materials: All data generated or analyzed during this study are included in this published article.

Ethics Approval: Ethical approval for the study was obtained from the Ethical Review Committee, University of Hail. Permission number: H-2024-351.

Conflicts of Interest: The authors declare no conflicts of interest to report regarding the present study.

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