National policy recommendation for early cancer prediction using artificial intelligence in Saudi Arabia: a review

Introduction

Background

Cancer has become a significant global health issue, spreading at an alarming rate. In 2022, the World Health Organization (WHO) reported approximately 20 million new cancer diagnoses, resulting in 9.7 million deaths across 115 countries (1). In 2020, the Saudi Cancer Registry (SCR) documented 17,631 newly identified cancer cases. Among these, 14,235 cases involved Saudi nationals, 3,274 cases involved non-Saudis, and 122 cases had unknown nationalities (2). In Saudi Arabia, there has been a notable increase in cancer cases, with projections suggesting a 116% rise from 2020 to 2040 (3).

Delayed cancer diagnosis is a worldwide systemic problem (4). In the Saudi context, various factors contribute to delays in cancer diagnosis, notably, one of them is the limited early detection programs and effective cancer detection strategies (4). A systematic review emphasizes the need for further research using validated instruments and targeting specific cancer types, which will ultimately enhance patient care in Saudi Arabia (5). A meta-analysis of 20 studies from 2000 to 2025 revealed that longer treatment delays are associated with higher mortality rates (6). This highlights the need for enhanced screening programs and awareness initiatives to facilitate the earlier identification and treatment of cancer cases, ultimately improving patient outcomes and reducing mortality rates.

The implementation of artificial intelligence (AI)-powered models has significantly improved the accuracy of medical decision-making (7,8). AI technologies have been employed in various roles for cancer patients. According to Zhan et al. (9), AI has effectively detected and diagnosed brain tumors. Additionally, Zhang et al. discovered that AI successfully predicted the timing and location of cancer recurrence in liver cancer patients (8). This approach aims to improve health outcomes and enhance patients’ overall quality of life by providing targeted therapies and optimized care strategies. Thus, AI’s role in personalizing cancer care exemplifies its transformative potential in modern medicine (10). Figure 1 illustrates the logical model of AI integration in early cancer detection and prediction.

Figure 1 The logical model of the AI-integration in early detection and prediction of cancer. It is presenting the key recommendations, contributions, and their advancement in promoting healthcare outcomes in Saudi Arabia. AI, artificial intelligence.

Current status of AI imaging models for cancer detection in Saudi Arabia

Saudi Arabia has made remarkable progress in creating and implementing sophisticated AI models for cancer detection through medical imaging. A prominent example is MiniGPT-Med, an advanced vision-interfaced AI model developed in collaboration between King Abdullah University of Science and Technology (KAUST) and the Saudi Data and Artificial Intelligence Authority (SDAIA). This model combines image analysis with clinical data to improve diagnostic accuracy for a range of diseases, including brain tumors, lung cancer, and pneumonia. MiniGPT-Med employs various imaging techniques such as X-rays, computed tomography (CT) scans, and magnetic resonance imaging (MRI) scans, showcasing superior performance compared to other leading models. Notably, it aids healthcare professionals by enhancing diagnostic precision and efficiency, reflecting Saudi Arabia’s dedication to incorporating AI technologies in healthcare (9-11).

Alongside MiniGPT-Med, various AI-based cancer imaging technologies are currently in use at prominent medical facilities in Saudi Arabia. For instance, the King Faisal Specialist Hospital and Research Centre has implemented AI systems that detect lung cancer through CT imaging, achieving an accuracy rate exceeding 90%. This advancement significantly improves early diagnosis and treatment results. Additionally, research institutions and hospitals in the Kingdom are developing convolutional neural network (CNN)-based models for breast cancer detection via mammograms, yielding encouraging outcomes in terms of both accuracy and practicality. These initiatives collectively underscore the growing presence and adoption of AI imaging technologies in Saudi Arabia, establishing the nation as a regional frontrunner in AI-enhanced medical diagnostics for cancer care (12-15).

Rationale and knowledge gap

The Kingdom of Saudi Arabia is undertaking systematic initiatives aimed at enhancing public awareness regarding cancer and promoting health-conscious practices among its population. Notably, the Ministry of Health (MOH) has launched the Breast Cancer Early Detection (BCED) project and a project targeting high-risk groups to early detect colorectal cancer by assessment and screening (16).

Despite the implementation of various initiatives, several factors continue to impede the timely diagnosis of cancer. Key contributors to these delays include the limited availability of early detection programs and the ineffectiveness of existing cancer detection strategies. These elements collectively hinder the efforts to facilitate early intervention and improve patient outcomes (5).

Although Saudi Arabia has made significant advancements in utilizing AI for healthcare diagnostics, it still does not have fully validated AI-driven systems for early cancer detection integrated into standard clinical practices. Innovative tools like Mirai, which predicts breast cancer, and MiniGPT-Med, used for multimodal diagnostics, are being tested and demonstrate promising accuracy; however, they are still in research or early pilot stages and have not been widely implemented or validated across various populations and cancer types in the country. Most AI models are being developed through isolated partnerships or limited clinical trials, lacking the comprehensive public health infrastructure (12,14). Saudi Arabia lacks policies to utilize AI and a lack of cultural readiness to embrace AI (17).

Therefore, there is a critical need for strong, clinically validated AI solutions that can be effectively rolled out nationwide to enhance early cancer detection and improve long-term outcomes in Saudi Arabia. The integration of AI can potentially enhance the prognosis, diagnosis, and treatment of various types of cancer. This advancement promises to improve overall healthcare quality for both cancer and non-cancer patients (7,8).

Objective

This study aimed to develop and recommend a national Saudi policy to promote the Saudi population’s health through early cancer prediction and detection using AI tools, specifically for the moderate to high-risk groups. The suggestions are grounded in addressing a fundamental question about how effective AI-driven tools are in the early detection and prediction of cancer within the Saudi population.

Methods

This research employed the eight steps of the standard Policy Development Process to structure and analyze the development of policy initiatives effectively. Each step was carefully followed to ensure a comprehensive approach to policy formulation, implementation, and evaluation (18). These steps are: identify the problem, assess community support, develop goals, objectives, and policy options, identify decision-makers and influencers, build support among decision-makers and stakeholders for the new policy, write and revise the policy, implement the policy, and assess and monitor the policy on an ongoing basis. The policy aims to be implemented in oncology centers, specialized oncology departments, and oncology service units throughout Saudi Arabia.

Results

This policy highlights the role of AI in Saudi Arabia’s healthcare system, particularly in detecting cancer in individuals who show no symptoms. The improved accuracy of AI surpasses traditional diagnostic techniques, leading to better healthcare outcomes and timely interventions. Additionally, this approach promotes more affordable healthcare solutions.

Key stakeholders include government bodies and health authorities, such as the MOH, local health department officials, healthcare providers, and tech companies that create AI algorithms. Secondary stakeholders involve patients and their families, oncology researchers, AI experts, hospital administrators, and insurance firms, all of whom play crucial roles in cancer detection and treatment.

Step 1: identify/describe/analyze the problem

A comprehensive understanding of the issue necessitates a clear definition of its root causes and implications for the community. Within the Saudi population, various factors contribute to the delay in cancer diagnosis and the subsequent development of cancer. Social determinants significantly influence this delay, including prevalent fears surrounding a cancer diagnosis, embarrassment associated with undergoing diagnostic procedures, and insufficient awareness regarding the disease. Additionally, a lack of effective cancer detection strategies further exacerbates the delay in diagnosis (5,19). According to the MOH, 75% of Saudi Arabia’s population does not undergo regular health screening (20). For instance, despite Saudi Arabia’s initiatives to detect breast cancer, the participation rate was surprisingly low, with only 8% of women attending screenings in 2015, and more than 50% of women demonstrated a lack of appropriate breast self-examination (21,22).

Numerous research studies have highlighted the negative consequences associated with delayed cancer diagnosis. This delayed diagnosis at an advanced stage can pose considerable challenges to effective treatment and management of the condition (23). Even the 2-week delay in diagnosing breast cancer increased the risk of death by up to 4% due to not performing breast cancer surgery (4). Moreover, the cost burden resulting from delayed cancer diagnosis, investigations, procedures, and treatment approaches places pressure on costs (24,25). An analysis examining the future trends of cancer incidence and its economic impact found that by 2030, cancer cases are predicted to increase by 63% compared to 2015 levels. This surge in cancer cases is expected to lead to a substantial rise in costs, with the economic burden projected to increase by 56% by 2030 relative to 2015 (24). On the other hand, early diagnosis is associated with minimizing therapeutic time, improving prognosis, and increasing survival rate (26).

The WHO underscores the urgent need for the development and implementation of policies aimed at reducing delays in cancer diagnosis on a global scale (2). The integration of AI in this domain holds significant promise. AI has been utilized to identify features indicative of early cancer development, thereby improving medical diagnosis capabilities and increasing the accuracy of cancer detection. According to Zhang et al., AI is positioned as an invaluable tool that can facilitate earlier cancer predictions compared to traditional diagnostic methods. Leveraging AI technology may substantially mitigate the challenges associated with delayed cancer diagnosis and improve patient outcomes (8).

A systematic review demonstrated that AI-based technologies are both feasible and accurate in detecting malignant cancers. A meta-analysis conducted in 2025 synthesized data from over 10,000 peer-reviewed studies, providing strong evidence of the therapeutic benefits of medical cannabis in cancer care (27). Furthermore, an umbrella review and meta-analysis published in March 2025 critically examined 158 systematic reviews on AI-driven image-based cancer diagnostics. The results show a notably high diagnostic accuracy for AI tools used in this field, highlighting their potential in clinical practice and their role in improving diagnostic processes (28). Additionally, AI-based clinical decision support systems have been proven to effectively detect oral cancer and improve patient outcomes (29).

To develop an effective policy addressing delayed cancer diagnosis, it is crucial to gather pertinent information regarding the existing readiness of programs and strategies aimed at mitigating this issue. Research indicates various potential solutions to enhance timely cancer detection, as emphasized by Hanna et al. (4), who advocated for the formulation of policies that specifically target the reduction of delays in cancer diagnosis.

Step 2: assess community support, capacity, and readiness to determine if the policy is an appropriate strategy

Define the Saudi community

The Kingdom of Saudi Arabia spans about 80% of the Arabian Peninsula, covering a total land area of approximately 2,000,000 square kilometers. It is divided into 13 regions (known as manatiq; singular, mintaqah): Al Bahah, Al Hudud ash Shamaliyah (Northern Border), Al Jawf, Al Madinah al Munawwarah (Medina), Al Qasim, Ar Riyad (Riyadh), Ash Sharqiyah (Eastern), Asir, Ha’il, Jazan, Makkah al Mukarramah (Mecca), Najran, and Tabuk (30). Saudi Arabia boasts a diverse geography, including forests, oasis, mountain ranges, and deserts (31). The total population is 36,544,431, comprising 20,700,838 males and 15,843,593 females, with a median age of 32.4 years. It is projected that by 2023, 85% of the population are urban (30). The SCR was established in 1992 as a population-based registry and is under the administration of the MOH. In 2014, the SCR was moved to the Saudi Health Council and now operates under the Department of National Registries within the National Center for Health Information (16).

Evaluate levels of community support, capacity, and readiness

Saudi Arabia is making concerted efforts to increase awareness about cancer and promote healthy practices. This initiative includes dispelling misconceptions about the disease, emphasizing the significance of regular screenings for both average and high-risk individuals, educating the public on the signs of cancer, and offering information about available screening and diagnostic resources.

Breast cancer

Saudi Arabia initiated several breast cancer awareness and detection programs in various regions. By 1997, screening projects were established in Dammam in the Eastern Province, Riyadh and Qasim in the Central Province, and Jeddah in the Western Province (32). In 2012, the MOH initiated a national BCED project. This project was conducted to enhance awareness about the modifiable breast cancer-associated factors and perform mammography on women at average risk (16). Moreover, mobile units are distributed around Saudi Arabia’s geographic areas to perform breast imaging by mammography; October is assigned yearly as breast cancer awareness month, and health facilities are available throughout the year for diagnostic studies related to breast cancer (16). The existing literature indicates that a substantial proportion of women exhibit a significant knowledge deficiency regarding breast cancer. This gap in understanding may influence early detection and treatment outcomes. Addressing this issue is critical in promoting proactive health behaviors and improving survival rates in breast cancer patients (22).

Colorectal cancer

In 2017, MOH initiated a project to detect colorectal cancer early. This project targeted the average to high-risk groups of developing colorectal cancer. Additionally, healthcare centers can provide healthcare assessment and diagnostic studies to detect colorectal cancer (16). However, despite the effort spent to enhance their awareness, a systematic review paper indicated that the Saudi population still demonstrates a lack of knowledge regarding colorectal cancer. Additionally, although health providers show a high level of knowledge, surprisingly, most did not perform the required screening (33).

Cervical cancer

A diagnostic procedure to rule out cervical cancer is available in Saudi Arabia’s healthcare sector; a pap smear test is open to every woman. However, awareness campaigns have yet to be established. Similarly, there are no established nationwide strategies for screening lung and prostate cancer. Diagnostic tools are available upon medical recommendations or per the patient’s request (16).

In Saudi Arabia, individuals have access to advanced imaging and laboratory diagnostic techniques for accurate and timely cancer diagnosis. For instance, the country offers widespread access to imaging facilities, boasting 81.7 mammographs, 251.6 CT scanners, and 158.5 MRI scanners per 10,000 cancer patients. Notably, there are 17 operational positron emission tomography (PET) CT machines in the Kingdom, with 13 located in Riyadh, 3 in Dammam, and 1 in Jeddah, ensuring access within 2–3 weeks. However, the expansion of PET CT scans is currently hindered by the limited availability of radiopharmaceutical substances and cyclotrons. Despite this challenge, a national initiative is in place to significantly increase the number of PET CT scans over the next 5 years (16). Laboratory investigations play an essential role in diagnosing cancer. Most laboratory tests are conducted in tertiary facilities around the kingdom. Saudi Arabia supports their population by granting laboratory investigations like molecular and genetic studies from Europe and the USA. However, some genetic studies have recently been performed in national tertiary healthcare centers. The industry supports national and international studies (16).

Step 3: develop goals, objectives, and policy options

Policy goals

This policy is designed to incorporate AI into the healthcare system for early prediction and detection of cancer. This goal harmonizes with Saudi Arabia’s health law, which strives to ensure accessible healthcare for every Saudi citizen (34). Additionally, it aligns with the new phase of the Health Sector Transformation Program, which emphasizes health promotion and the facilitation of healthcare services, including utilizing digital transformation as part of the Kingdom’s Vision 2030 programs (35).

Policy objectives

The objectives of the national policy for early cancer prediction using AI in Saudi Arabia are to:

• Determine the national AI-based technology readiness;
• Prepare AI specialists to train AI algorithms and programs to predict and detect cancer features in a symptomatic individual;
• Train health providers to be capable of utilizing AI in health settings;
• To promote an effective decision-making strategy regarding the treatment plan;
• To reduce the delayed cancer diagnosis;
• Motivate the body of research combining clinical and AI evidence-based research.

Policy options

This study identifies four policy options for integrating AI in the prediction and detection of cancer within the Saudi population. These options aim to enhance early diagnosis, improve treatment outcomes, and optimize resource allocation in the healthcare system. Each policy proposal considers the unique cultural, economic, and healthcare landscape of Saudi Arabia, ensuring that the implementation of AI technologies aligns with national objectives and public health needs. The proposed options are expected to foster collaboration among healthcare providers, government entities, and technology developers, ultimately leading to a more efficient and effective cancer care framework.

• Option 1: maintain the current strategies for cancer diagnosis without any modifications;
• Option 2: implement targeted modifications to the current Health Sector Transformation strategies, focusing on specific areas that require improvement or adaptation;
• Option 3: revise the existing policy to implement a comprehensive new framework that better aligns with our organizational goals and values;
• Option 4: leveraging AI algorithms in diagnostic imaging to identify tissue characteristics that may suggest the presence of cancer is both a politically and economically viable approach.

Evaluation criteria

Following policy identification and determining the policy, objectives, options, and alternatives, the next step is narrowing these options to one concise, achievable option. All options should be evaluated according to specific criteria to reach this step. The criteria identify how the objectives will be measured and how the alternatives are better (36).

This paper has selected five criteria to evaluate the practicality and applicability of the pre-identified policy options: economic, political, technical, equity, and administrative. Most policies focus on economic criteria, considering costs and benefits, aiming to assess their impact on the economy and government expenditures. Regarding political criteria, a key question is how the alternative options align with the interests of decision-makers and stakeholders. This can be evaluated by examining the accessibility of the policy to the relevant groups, the alignment of the alternative with the values and beliefs of the target community, its compliance with legal requirements, and its responsiveness to the needs of the target demographic (36).

Another criterion is technical, which indicates whether AI technology is available, feasible, and ready to be employed. The readiness includes the availability of AI specialists and program-based computers and the preparedness for integration (36). Equity is a fundamental principle that emphasizes the fair and impartial distribution of policy services to every individual within the Saudi population. It ensures that all members of society have an equal opportunity to access and benefit from the policy, regardless of their background or circumstances (36). The last criterion is the administrative. Many questions arise from this criterion. The Administration is concerned with identifying if MOH and SDAIA have the authority to implement this policy, if the managers, staff, and other manpower are committed to implementing this policy, if there is appropriate capacity using the availability of staff, expertise, and training, and if the support of the facilities and the equipment (36).

Analyzing and comparing policy options

This section’s analysis is grounded in the evaluation criteria outlined previously, facilitating a structured and systematic comparison of the different policy options. Option 1: if political changes do not occur, the economic burden is likely to worsen. Additionally, the cancer epidemic is expected to escalate, and the issues surrounding delayed diagnoses will continue, resulting in postponed treatments and a decline in survival rates among cancer patients. This scenario is not aligned with the goals of Health Sector Transformation, which prioritizes health promotion services and advocates for the adoption of digital strategies (35). Option 2: the feasibility of implementation is supported by both political and economic considerations. Additionally, it achieves social acceptance by aligning with broader health promotion goals, thereby benefiting individuals across the spectrum, from average to high-risk populations. Option 3: while the new policy holds promising prospects, it must undergo a series of protocols and formal acceptance processes to be realized. This option may encounter resistance to change and require additional implementation time. It is both politically and economically impractical. Option 4: the capacity of AI to produce highly accurate results has the potential to reduce the necessity for redundant diagnostic testing. This efficiency may serve to motivate individuals to pursue essential medical investigations, ultimately enhancing patient compliance and health outcomes. By streamlining the diagnostic process, AI could play a pivotal role in encouraging timely and appropriate healthcare interventions (see Table 1).

Challenges and limitations of AI in Saudi Arabia

However, the adoption of AI diagnostic systems in Saudi Arabia’s healthcare sector encounters various challenges and constraints that need to be thoughtfully addressed to guarantee accurate, dependable, and ethical patient care. These challenges include the possibility of false positives and negatives in AI diagnoses, which could result in unnecessary treatments or the failure to detect diseases. AI model outcomes may seem overly optimistic if adequate data splitting and validation methods are not utilized, highlighting the importance of thorough model assessment for achieving high reliability and performance. Additionally, incorporating AI technologies into current clinical practices is complex, as it necessitates compatibility with existing systems and acceptance from healthcare professionals. There are also ethical and regulatory issues, especially concerning patient data privacy, transparency of algorithms, and reducing bias (17). Comprehensive efforts are essential to establish national standards, develop solid quality assurance processes, and provide ongoing training to promote AI integration and ensure patient safety in the Saudi context.

Step 4: identify decision-makers and influencers

This policy involves four key decision-makers: the MOH, which is tasked with health promotion, cancer screening, and the execution of cancer awareness campaigns and programs. The SDAIA has been established at the national level to advance data and AI strategies. Healthcare providers and leaders within hospitals and clinics will adopt and implement AI technologies for the detection and treatment of cancer. Additionally, research institutions will engage researchers in developing AI algorithms, platforms, and applications aimed at enhancing the prediction, detection, and treatment of cancer. These stakeholders collaborate to shape policies that leverage AI technologies to improve the early prediction and detection of cancer before it manifests.

Step 5: build support among decision makers and stakeholders for the new policy

Following the identification of a new policy by stakeholders and decision-makers, it is essential to secure the necessary support for implementation. To achieve this, we must effectively engage the stakeholders who are impacted by the policy and the decision-makers who possess the authority to enact it. Capturing their attention and demonstrating the policy’s significance is crucial for fostering buy-in and facilitating a successful rollout.

A written letter will be sent to the stakeholders and policymakers in MOH and SDAIA, addressing accurate information about the policy: its purpose, indications, and the consequences of not conducting it. We should grasp the attention of the decision-makers and the stakeholders, and the significance of this policy should be stated explicitly. They should be aware of the options for this policy, analysis, and alternative plans. Imaging investigations will be extracted from a national database after obtaining permission and arrangement from the MOH. SDAIA will assign the database to AI specialists, who will train the AI models to identify features of tissue that could turn into cancer. This step will help individuals in the average and high-risk groups have accurate early cancer predictions. Health professionals treating oncology patients should be trained by integrating AI models into imaging studies. They will be trained about the detected features, the percentage, and the timing of developing cancer.

To facilitate the effective and culturally sensitive implementation of AI-driven cancer detection in Saudi Arabia, this study emphasizes the importance of engaging patients, healthcare providers, and AI developers throughout the deployment process. Collaborative co-design workshops will be arranged to unite stakeholders to jointly define system requirements that meet both clinical and cultural considerations. Additionally, usability testing sessions will take place, featuring real-world simulations with end users to refine interface design, integrate workflows, and enhance user satisfaction. The development of the system will adhere to human factors engineering principles, which prioritize safety, trust, and accessibility specific to the Saudi healthcare environment. This participatory methodology is expected to promote technology adoption, build trust among stakeholders, and improve the overall effectiveness of the system (37).

Ethics and data privacy

In addition to complying with Saudi Arabia’s Personal Data Protection Law and regulations related to data governance, ethical oversight, patient consent, and data privacy in national imaging databases, the international FUTURE-AI framework will guide these efforts. This framework emphasizes the need for trustworthy and implementable AI in healthcare, outlining six core principles: fairness, universality, traceability, usability, robustness, and explainability. These principles promote ethical and responsible AI system implementation throughout their lifecycle, including best practices for collaboration among stakeholders, transparent consent processes, strong privacy safeguards, continuous bias monitoring, and clear documentation and accountability mechanisms. By adopting the FUTURE-AI guidelines, this policy seeks to align with international standards and national regulations, uphold patient rights, and build trust in AI-driven cancer imaging projects in Saudi Arabia (38,39).

Step 6: write and revise the policy

The national policy for early cancer prediction utilizing AI in Saudi Arabia aims to implement technological strategies for the early prediction and detection of cancer among the Saudi population, particularly within average and high-risk groups. To realize this policy, several key actions will be undertaken: assessing the readiness of AI-based technologies, training AI specialists to develop models for cancer prediction and detection, equipping designated healthcare professionals to effectively utilize AI technology in cancer diagnosis and treatment, promoting effective decision-making strategies concerning cancer treatment, minimizing delays in cancer diagnosis, and encouraging the integration of clinical research studies focused on AI. This policy also provides a comprehensive overview of the benefits it offers to the national economy and the health of the Saudi population. Additionally, it will outline the role of the MOH and the SDAIA, as well as the nature of their collaboration.

Step 7: implement the policy

• Grant the support of MOH to provide imaging from the public database of individuals who have had a normal imaging interpretation for 3 years and repeated the same imaging for the current year.
• For the current year, collect imaging of the same individuals and categorize them as turning to cancer or remaining normal.
• The images of the two specified intervals will be submitted to the SDAIA. Subsequently, SDAIA will designate trained AI specialists to input these imaging data into the AI system. This process aims to train the model to accurately identify the histological features that transitioned from a normal state to a malignant one, as well as those characteristics that remain within the normal range.
• The AI model will be tested in three hospitals in the Eastern Province to determine its accuracy and readiness. Modifications will be applied based on the findings.
• MOH and SDAIA will approve the final model. Both organizations will establish an agreement identifying each other’s roles, methods of collaboration, required resources, and implementation approaches.
• Health professionals responsible for oncology patients will be trained to utilize the AI model in predicting, detecting, and treating cancer. Training will be provided by admitting them to courses conducted by SDAIA. However, the Saudi Health Council may design a 1-year program to certify health professionals to confirm their preparedness.
• Publish the policy to all oncology and tertiary facilities in Saudi Arabia.
• The policy will be disseminated to the public through an awareness campaign, the official websites of MOH and SDAIA, posters in hospitals and public places, and emails at workplaces.
• Make mandatory oncology screening protocols for all the average high-risk groups.

Step 8: assess and monitor the policy on an ongoing basis

Upon policy implementation, descriptive analysis will be conducted to assess the number and percentage of detected cases in the early stage and the predicted ones. A committee will be established to monitor the progress of the suggested policy, consisting of members from the four Provinces (Eastern, Central, North, and Western). These members are key persons from MOH, SDAIA key representatives, oncologists, radiologists, nurse administrators, average and high-risk group individuals, a research team, including a statistician, a member from the Saudi Council, and a secretary. The committee will set a timeline, evaluation, and communication guidelines to monitor the progress, the outcomes, and the achievements of the objectives. These outcomes will be compared with the national outcomes for the last 10 years. To examine the effectiveness and improvement plans, a qualitative study will enroll the stakeholders to explore their opinions about the policy, and an action plan will be designed accordingly.

The role of nursing research on early cancer prediction & detection in Saudi Arabia?

Nursing research is crucial in identifying nurses’ roles in caring for oncology patients. To effectively predict, detect, and provide treatment early, nurses must be well-oriented about their responsibilities in the oncology setting, from prediction to cancer treatment. Implementing AI integration policy, training programs, and awareness can improve preventive and therapeutic measures for average and highly developed cancer groups. Therefore, the practical AI determination of pre-cancerous tissue features of highly developed cancer groups and the utilization of research findings should guide the design of a national policy implemented by health providers and empowered by stakeholders. Moreover, the research highlights the needs and points requiring stakeholder attention.

Discussion

This research has formulated and proposed a national policy aimed at the early detection and prediction of cancer cases among Saudi Arabian citizens. Four policy options were identified and evaluated against five criteria: economic, political, technical, equity, and administrative. The evaluation revealed that the fourth option, which advocates for integrating AI in early cancer detection and prediction, is the most appropriate policy. Despite significant efforts by Saudi Arabia to enhance cancer awareness among its population, challenges related to delayed cancer diagnosis continue to exist. This policy is designed to promote early detection and prediction of cancer, which may not be achievable through traditional diagnostic methods. Furthermore, the accuracy and speed of AI algorithms could encourage the population to pursue diagnostic testing more actively.

The suggested national policy in Saudi Arabia for early cancer detection through AI is influenced by the Kingdom’s Vision 2030, which highlights the importance of digital advancements, healthcare innovation, and coherence with local values and infrastructure (40,41). Comparing it to international guidelines and frameworks, several similarities and differences were detected. Like the FUTURE-AI international consensus guideline, which advocates for trustworthy, ethical, and transparent AI in healthcare, the Saudi policy prioritizes data privacy, patient safety, and the responsible use of AI in healthcare (38,42). Both frameworks acknowledge the significance of establishing strong governance structures and integrating ongoing evaluation processes to guarantee the safe deployment of AI technologies. However, our policy approach distinctly prioritizes the enhancement of national capacity, particularly through strategic investments in digital infrastructure and comprehensive workforce training programs for health providers and AI employees. This focus aims to effectively address existing skills gaps within the local population, ensuring that the workforce is equipped to meet the demands of a rapidly evolving economic landscape.

While international guidelines often focus on broad, cross-border standards. For instance, the FUTURE-AI framework was designed by experts from 50 countries to provide a universal AI-based framework (38). On the other hand, the Saudi policy is tailored to the national context, addressing challenges such as disparities in digital skills among healthcare professionals, regional differences in technology access, and the need for culturally relevant ethical frameworks.

International documents, such as those from the WHO, emphasize the significance of worldwide collaboration and standardization (43). In contrast, the current policy integrates the objectives of Vision 2030, aiming to localize technology and promote the health of the Saudi population. Moreover, globally, AI guidelines highlight the significance of ongoing enhancement, openness, and flexibility as technology progresses (38). Similarly, our policy and another Saudi study reflect these values while additionally emphasizing the need to align AI projects with national strategic goals, including the development of human capital and healthcare equity outlined in Vision 2030 (41).

Formulating a national policy for early cancer prediction utilizing AI in Saudi Arabia marks a crucial advancement in transforming the Kingdom’s healthcare system. Early cancer detection plays a vital role in enhancing survival rates and alleviating the overall impact of the disease. The incorporation of AI technologies into cancer prediction aligns with Saudi Arabia’s Vision 2030, which prioritizes innovation, digital transformation, and the elevation of healthcare service quality (35).

This policy targets deficiencies in early cancer detection by establishing a cohesive framework that promotes using AI-powered tools throughout healthcare organizations. Although various international guidelines are available (8,38,44,45), Saudi Arabia’s distinct demographic characteristics, healthcare systems, and regulatory landscape require a customized method. By prioritizing national requirements, this policy guarantees that AI solutions are not only culturally suitable but also technically viable within the local framework.

The expected effect of enacting this policy is significant. AI-driven predictive models can process large volumes of clinical and imaging data, allowing for the earlier detection of individuals at high risk and enabling prompt interventions. This could lead to a decrease in late-stage cancer diagnoses, reduced healthcare expenses, and enhanced patient outcomes. Furthermore, the policy encourages collaboration among governmental bodies, healthcare professionals, technology innovators, and academic institutions, nurturing a culture of innovation and ongoing development.

Strengths and limitations

This policy was formulated based on a structured eight-step process that emphasizes the collaboration between AI specialists and medical teams, facilitated by two major institutions in Saudi Arabia: the MOH and the SDAIA. However, there are several limitations to consider. These include barriers to implementation, such as a lack of awareness among the Saudi population regarding AI and its role, apprehension towards new policies, and a general mistrust of digital integration. Additionally, there is a shortage of trained healthcare providers capable of utilizing AI algorithm tools, which could hinder effective implementation. Moreover, this study did not assess the required resources and costs.

Recommendations

Considering the practicality and variations among the stakeholders involved, and to maximize the advantages of the policy execution as well as enhance its effects, recommendations are offered for patients, health students, and healthcare practitioners. We recommend conducting periodic awareness campaigns in hospitals and public areas to increase awareness of the moderate-to-high-risk population and their families (46). These initiatives will take into account the unique aspects of Saudi culture by ensuring privacy and separating genders as needed, while also adhering to Islamic principles in the dissemination of information.

Additionally, we propose incorporating AI and its diverse applications in medical diagnosis into university academic curricula. By embedding this cutting-edge technology into the educational framework, students will not only gain a deeper understanding of AI algorithms and data analysis techniques but also develop practical skills for leveraging these tools in real-world healthcare settings. This integration will prepare future healthcare professionals to harness the power of AI to improve diagnostic accuracy, streamline patient care, and ultimately enhance health outcomes (47). Education must be paired with the assurance that human potential will be prioritized, empowered rather than diminished by digital integration, taking into account the students’ values and reassuring them that they will not supplant them.

Training healthcare staff to effectively use AI tools for diagnostic procedures is crucial for enhancing patient care and improving diagnostic accuracy. This can be achieved through a comprehensive framework that includes workshops, presentations, and specialized training programs. Such initiatives should be accompanied by the distribution of educational materials that provide in-depth insights and practical guidance on these technologies. By equipping healthcare professionals with the necessary skills and knowledge, the integration of AI into clinical practice can lead to more informed decision-making and better health outcomes (48). The organization of staff training must be carefully considered to avoid overburdening employees with additional hours, especially during their time off. The administration must collaborate effectively to allocate sufficient time and resources for staff training, ensuring that employees can participate without undue stress or disruption to their work-life balance.

The recommendations outlined in this policy are informed by evidence from reliable and reputable published studies, chosen for their relevance and scientific rigor. However, we did not perform a formal systematic review or a structured assessment of evidence certainty, such as the GRADE approach. Consequently, although the supporting evidence is deemed to be of moderate to high quality based on the reputation of the sources, the overall certainty of the evidence and the strength of the recommendations should be interpreted with caution.

The financial and resource impacts were not systematically assessed during the formulation of these recommendations. There was no analysis of cost-effectiveness or economic evaluation conducted for the suggested interventions, which include awareness campaigns, training workshops, and the inclusion of AI courses in universities. The recommendations were established based on literature evidence, perceived public health importance, and practicality, rather than a structured economic evaluation.

Conclusions

In summary, establishing a national policy that prioritizes early detection and predictive measures for cancer through AI is crucial. This initiative requires collaboration between the MOH and the SDAIA. It aligns with Vision 2030’s objectives to enhance the quality of life for affected populations. It promises significant benefits for both MOH and SDAIA, contributing to a healthier and more technologically advanced society. Future research should investigate the policy’s effects on patients and healthcare providers and develop diagnostic models for identifying precancerous features in imaging data while evaluating necessary resources and costs for implementation.

Acknowledgments

None.

Footnote

Peer Review File: Available at https://jmai.amegroups.com/article/view/10.21037/jmai-2025-139/prf

Funding: None.

Conflicts of Interest: Both authors have completed the ICMJE uniform disclosure form (available at https://jmai.amegroups.com/article/view/10.21037/jmai-2025-139/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part are appropriately investigated and resolved.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

References

1. World Health Organization. Global cancer burden growing, amidst mounting need for services. 2024 [cited 2025 May 24]. Available online: https://www.who.int/news/item/01-02-2024-global-cancer-burden-growing--amidst-mounting-need-for-services
2. Saudi Health Council. Cancer Incidence Report: Saudi Arabia 2020. 2022. Available online: https://shc.gov.sa/Arabic/NCC/Activities/AnnualReports/Cancer%20Incidence%20Report%202020.pdf
3. Alessy SA, Alqahtani SA, Vignat J, et al. The current and future cancer burden in the Gulf Cooperation Council (GCC) countries. Cancer Med 2024;13:e70141. [Crossref] [PubMed]
4. Hanna TP, King WD, Thibodeau S, et al. Mortality due to cancer treatment delay: systematic review and meta-analysis. BMJ 2020;371:m4087. [Crossref] [PubMed]
5. Althemery AU, Alzahrani R, Alajlan N, et al. Quality of life for patients on oncology treatments in the Kingdom of Saudi Arabia: a systematic review. J Pharm Policy Pract 2025;18:2449036. [Crossref] [PubMed]
6. Ungvari Z, Fekete M, Fekete JT, et al. Treatment delay significantly increases mortality in colorectal cancer: a meta-analysis. Geroscience 2025;47:5337-53. [Crossref] [PubMed]
7. Silva HECD, Santos GNM, Leite AF, et al. The use of artificial intelligence tools in cancer detection compared to the traditional diagnostic imaging methods: An overview of the systematic reviews. PLoS One 2023;18:e0292063. [Crossref] [PubMed]
8. Zhang B, Shi H, Wang H. Machine Learning and AI in Cancer Prognosis, Prediction, and Treatment Selection: A Critical Approach. J Multidiscip Healthc 2023;16:1779-91. [Crossref] [PubMed]
9. Zhan Y, Hao Y, Wang X, et al. Advances of artificial intelligence in clinical application and scientific research of neuro-oncology: Current knowledge and future perspectives. Crit Rev Oncol Hematol 2025;209:104682. [Crossref] [PubMed]
10. Bhinder B, Gilvary C, Madhukar NS, et al. Artificial Intelligence in Cancer Research and Precision Medicine. Cancer Discov 2021;11:900-15. [Crossref] [PubMed]
11. Malin C. New Saudi AI model to advance radiology diagnosis. 2025 [cited 2025 Aug 25]. Available online: https://www.middleeastainews.com/p/new-saudi-ai-model-radiology-kaust
12. Rafizadeh M, Abou K. How a cutting-edge AI model developed in Saudi Arabia is reshaping medical diagnostics. 2024 [cited 2025 Aug 23]. Available online: https://www.arabnews.com/node/2568476/saudi-arabia
13. Omari E. Saudi's Healthcare AI Integration Assessing the Efficacy of AI-Driven Diagnostics and Patient Management Systems. [Cited 2025 Aug 25]. Available online: https://www.tanmeya.com.sa/article/saudis-healthcare-ai-integration-assessing-the-efficacy-of-ai-driven-diagnostics-and-patient-management-systems
14. Community Jameel. Jameel Clinic at MIT and KFSH&RC launch pioneering AI early breast cancer detection tool in Saudi Arabia. 2023 [cited 2025 Aug 23]. Available online: https://www.communityjameel.org/media/jameel-clinic-at-mit-and-kfsh-rc-launch-pioneering-ai-early-breast-cancer-detection-tool-in-saudi-arabia
15. Almalki YE, Shaf A, Ali T, et al. Breast cancer detection in Saudi Arabian women using hybrid machine learning on mammographic images. Computers, Materials & Continua 2022;72:4833-51.
16. Abusanad A, Alghamdi M, Bakkar M, et al. General oncology care in the Kingdom of Saudi Arabia. In: Al-Shamsi HO, Abu-Gheida IH, Iqbal F, et al. editors. Cancer in the arab world. Singapore: Springer Singapore; 2022:215-33.
17. Aljehani NM, Al Nawees FE. The current state, challenges, and future directions of artificial intelligence in healthcare in Saudi Arabia: systematic review. Front Artif Intell 2025;8:1518440. [Crossref] [PubMed]
18. World Health Organization. Module 4: Understanding the policy, political and decision-making processes. 2000. Available online: https://iris.who.int/bitstream/handle/10665/207061/9290611863_mod4_eng.pdf?sequence=3&isAllowed=y
19. Alsadhan N, Brennan C, Alhurishi SA, et al. Factors influencing colorectal cancer screening decisions among Saudi women: A qualitative study. PLoS One 2025;20:e0321086. [Crossref] [PubMed]
20. Gosadi IM. National screening programs in Saudi Arabia: Overview, outcomes, and effectiveness. J Infect Public Health 2019;12:608-14. [Crossref] [PubMed]
21. AlSaleh KA. Efficacy of breast cancer screening program in Kingdom of Saudi Arabia. Saudi Med J 2022;43:428-30. [Crossref] [PubMed]
22. Al Otaibi Sh, Al Harbi M, Al Kahmoas A, et al. General Breast Cancer Awareness among Women in Riyadh City. Asian Pac J Cancer Prev 2017;18:159-63. [Crossref] [PubMed]
23. Hoveling LA, Schuurman M, Siesling S, et al. Diagnostic delay in women with cancer: What do we know and which factors contribute? Breast 2025;80:104427. [Crossref] [PubMed]
24. Jazieh AR, Da'ar OB, Alkaiyat M, et al. Cancer Incidence Trends From 1999 to 2015 And Contributions Of Various Cancer Types To The Overall Burden: Projections To 2030 And Extrapolation Of Economic Burden In Saudi Arabia. Cancer Manag Res 2019;11:9665-74. [Crossref] [PubMed]
25. McGarvey N, Gitlin M, Fadli E, et al. Increased healthcare costs by later stage cancer diagnosis. BMC Health Serv Res 2022;22:1155. [Crossref] [PubMed]
26. Bhushan A, Gonsalves A, Menon JU. Current State of Breast Cancer Diagnosis, Treatment, and Theranostics. Pharmaceutics 2021;13:723. [Crossref] [PubMed]
27. Castle RD, Marzolf J, Morris M, et al. Meta-analysis of medical cannabis outcomes and associations with cancer. Front Oncol 2025;15:1490621. [Crossref] [PubMed]
28. Xu HL, Gong TT, Song XJ, et al. Artificial Intelligence Performance in Image-Based Cancer Identification: Umbrella Review of Systematic Reviews. J Med Internet Res 2025;27:e53567. [Crossref] [PubMed]
29. Karuppan Perumal MK, Rajan Renuka R, Kumar Subbiah S, et al. Artificial intelligence-driven clinical decision support systems for early detection and precision therapy in oral cancer: a mini review. Front Oral Health 2025;6:1592428. [Crossref] [PubMed]
30. Central Intelligence Agency. The World Factbook. 2025 [cited 2025 May 25]. Available online: https://www.cia.gov/the-world-factbook/countries/saudi-arabia/
31. Ministry of Foreign Affairs – Kingdom of Saudi Arabia. About Kingdom. Riyadh (Saudi Arabia): Ministry of Foreign Affairs; [cited 2025 Dec 7]. Available online: https://www.mofa.gov.sa/en/ksa/Pages/default.aspx
32. Knoll SM. Breast Cancer screening and a comprehensive breast cancer program in Saudi Arabia. Ann Saudi Med 1997;17:1-3. [Crossref] [PubMed]
33. Alshaer O, Binobaid A, Alqahtani M, et al. Colorectal awareness in Saudi Arabia: A systematic review of cross-sectional studies. J Healthc Sci 2021;1:66-76.
34. Ministry of Health, Saudi Arabia. Health Law Implementing Regulations. 2023. Available online: https://www.moh.gov.sa/en/Ministry/Rules/Documents/002.pdf
35. Vision 2030. Health Sector Transformation Program. [Cited 2025 May 24]. Available online: https://www.vision2030.gov.sa/en/explore/programs/health-sector-transformation-program
36. Bardach E, Patashnik EM. A practical guide for policy analysis: The eightfold path to more effective problem solving. 6th ed. Thousand Oaks: CQ Press, 2019.
37. Kumar R, Singh A, Subahi A, et al. Leveraging artificial intelligence to achieve sustainable public healthcare services in Saudi Arabia: a systematic literature review of critical success factors. Computer Modeling in Engineering & Sciences 2025;142:1289.
38. Lekadir K, Frangi AF, Porras AR, et al. FUTURE-AI: international consensus guideline for trustworthy and deployable artificial intelligence in healthcare. BMJ 2025;388:e081554. [Crossref] [PubMed]
39. Saudi Data & AI Authority (SDAIA). Personal Data Protection Law. 2023. Available online: https://sdaia.gov.sa/en/SDAIA/about/Documents/Personal%20Data%20English%20V2-23April2023-%20Reviewed-.pdf
40. Mani ZA, Goniewicz K. Transforming healthcare in Saudi Arabia: a comprehensive evaluation of vision 2030’s impact. Sustainability 2024;16:3277.
41. Muafa AM, Al-Obadi SH, Al-Saleem NAI, et al. The impact of artificial intelligence applications on the digital transformation of healthcare delivery in Riyadh, Saudi Arabia (opportunities and challenges in alignment with vision 2030). Academic Journal of Research and Scientific Publishing 2024;5:61-102.
42. El-Khoury M, Albarashdi S. Navigating the privacy landscape of healthcare-driven AI in the Middle East: Case studies from Oman, Qatar, and Saudi Arabia. Social Sciences & Humanities Open 2025;11:101492.
43. World Health Organization. WHO announces new collaborating centre on AI for health governance. 2025 [cited 2025 May 25]. Available online: https://www.who.int/europe/news/item/10-03-2025-who-announces-new-collaborating-centre-on-ai-for-health-governance
44. 1870 Ventures. An Ethical Map of the Future: AI Regulation and Healthcare Innovation. 2024 [cited 2025 May 25]. Available online: https://www.1870ventures.com/article/an-ethical-map-of-the-future-ai-regulation-and-healthcare-innovation
45. Australasian Institute of Digital Health. Roadmap recommendations for safe, ethical, sustainable and personalised AI in healthcare. 2023 [cited 2025 May 25]. Available online: https://digitalhealth.org.au/blog/roadmap-recommendations-for-ai-in-healthcare/
46. Apollo Hospitals. The Role of Artificial Intelligence in Early Cancer Detection: A Game-Changer for Patients at Apollo Precision Oncology Centre. 2025 [cited 2025 May 27]. Available online: https://www.apollohospitals.com/health-library/the-role-of-artificial-intelligence-in-early-cancer-detection-a-game-changer-for-patients-at-apollo-precision-oncology-centre
47. Pohn B, Mehnen L, Fitzek S, et al. Integrating artificial intelligence into pre-clinical medical education: challenges, opportunities, and recommendations. Front Educ 2025;10:1570389.
48. Singh Y, Patel H, Vera-Garcia DV, et al. Beyond the hype: Navigating bias in AI-driven cancer detection. Oncotarget 2024;15:764-6. [Crossref] [PubMed]