A Systematic Review of the Outcomes of Utilization of Artificial Intelligence Within the Healthcare Systems of the Middle East: A Thematic Analysis of Findings

ABSTRACT

Background and Aims

The rapid expansion of artificial intelligence (AI) within worldwide healthcare systems is occurring at a significant rate. In this context, the Middle East has demonstrated distinctive characteristics in the application of AI within the healthcare sector, particularly shaped by regional policies. This study examined the outcomes resulting from the utilization of AI within healthcare systems in the Middle East.

Methods

A systematic review was conducted across several databases, including PubMed, Scopus, ProQuest, and the Cochrane Database of Systematic Reviews in 2024. The quality assessment of the included studies was conducted using the Authority, Accuracy, Coverage, Objectivity, Date, Significance checklist. Following this, a thematic analysis was carried out on the acquired data, adhering to the Boyatzis approach.

Results

100 papers were included. The quality and bias risk of the included studies were delineated to be within an acceptable range. Multiple themes were derived from the thematic analysis including: “Prediction of diseases, their diagnosis, and outcomes,” “Prediction of organizational issues and attributes,” “Prediction of mental health issues and attributes,” “Prediction of polypharmacy and emotional analysis of texts,” “Prediction of climate change issues and attributes,” and “Prediction and identification of success and satisfaction among healthcare individuals.”

Conclusion

The findings emphasized AI's significant potential in addressing prevalent healthcare challenges in the Middle East, such as cancer, diabetes, and climate change. AI has the potential to overhaul the healthcare systems. The findings also highlighted the need for policymakers and administrators to develop a concrete plan to effectively integrate AI into healthcare systems.

Summary

• AI has a substantial potential in mitigating the challenges posed by cancer and diabetes in the Middle East.

• In situations where the healthcare system is strained, such as during a pandemic, AI can provide an effective solution.

• AI enhances the analysis of patient data, accelerating early diagnosis and treatment processes, while increasing the efficiency of healthcare delivery and optimal use of resources.

• AI can be utilized to confront the climate change and other similar challenges.

• There is a lack of studies concerning the effects of AI utilization in nonclinical facets of healthcare service delivery, encompassing areas such as human resource management and economics

1. Introduction

Artificial Intelligence (AI) denotes the advancement of computational systems capable of executing tasks traditionally reliant on human intelligence, such as pattern recognition, data‐driven learning, and decision‐making processes. Within the medical realm, AI stands poised to streamline workloads, elevate patient care standards, and optimize overall outcomes by aiding in critical functions such as treatment assessments and medical image analyses [1, 2].

Currently, the proliferation of AI within global healthcare systems is occurring at a notable pace. AI technologies are being progressively adopted across multiple medical domains, encompassing diagnostics, patient management, and hospital administration [3]. In this regard, the COVID‐19 pandemic has expedited the integration of digital technologies, including AI, within healthcare systems [4].

AI is widely regarded as a catalyst for enhancing various aspects of healthcare operations and service delivery. Forecasts indicate substantial cost savings of up to $150 billion annually in the United States by 2026. Additionally, AI facilitates a transition from a reactive healthcare paradigm to a proactive model, emphasizing health management as opposed to mere disease treatment [5].

AI possesses the capability to transform the landscape of healthcare by bolstering diagnostic precision, ameliorating patient prognoses, refining treatment strategies, mitigating healthcare expenditures, and tailoring medical interventions according to individual attributes such as genetic profiles, physiological metrics, and environmental determinants. AI's potential contributions span across a multitude of healthcare domains, encompassing administrative processes, medical imaging analyses, robotic surgical procedures, virtual assistant applications, clinical decision‐support systems, and precision medicine initiatives. This integration of AI‐enabled functionalities not only promises to create a healthcare framework characterized by enhanced efficiency and effectiveness in service delivery but also one that is highly adept at detecting false and unverified data [1, 6, 7, 8].

Multiple studies have indicated that although there exists a favorable disposition towards the imperative role of AI in the medical sector among the population residing in the Middle East, there remains a dearth of comprehension regarding its practical implementation and overarching significance [9, 10]. The Middle East constitutes a geographical region delineated to encompass the nations of Bahrain, Egypt, Iraq, Jordan, Kuwait, Lebanon, Oman, Qatar, Saudi Arabia, Syria, the United Arab Emirates, Yemen, Gaza, and the West Bank, as well as Iran and Turkey [11].

A study in the UAE found that physicians recognize both enabling and hindering factors in adopting AI applications for patient care, emphasizing the need for control, training, and interpretability of AI outcomes. Besides that, the Dubai Health Authority has implemented an AI policy to foster collaboration among healthcare stakeholders to enhance services [12]. In Saudi Arabia, the government is actively integrating AI into healthcare to improve service quality and address challenges like chronic diseases, although issues such as data quality and regulatory frameworks still need to be resolved [13]. In this regard, Saudi Arabia's Vision 2030 places significant emphasis on the integration of AI within the healthcare sector. This initiative is supported by the establishment of the Saudi Data and Artificial Intelligence Authority (SDAIA), which is tasked with leveraging data to enhance healthcare outcomes [14]. Therefore, Exploring and delineating the outcomes resulting from the integration of artificial intelligence across various facets of healthcare systems can serve as an effective strategy to bolster its utilization among the inhabitants of the region.

The Middle East faces several challenges in adopting AI in healthcare, including inadequate data quality and infrastructure, which hinder the implementation of AI solutions across medical specialties [3]. There is also a critical need for training healthcare professionals and raising awareness among providers and patients to foster acceptance of AI technologies [13, 15]. Additionally, concerns about equity highlight the risk that AI may exacerbate existing inequalities in healthcare access, necessitating careful consideration by policymakers to ensure that all populations benefit from these innovations [12, 16].

At the juncture of drafting this manuscript, no systematic reviews had been undertaken to scrutinize the ramifications stemming from the incorporation of AI within the healthcare systems of the Middle East. Nonetheless, within the existing literature, a solitary study emerged as a meta‐analysis, probing into the deployment of AI applications within the United Arab Emirates to mitigate and confront the COVID‐19 pandemic. The study revealed that AI applications proved efficacious in curbing the transmission of COVID‐19, surveilling adherence to imposed restrictions and preventive protocols, and furnishing remote healthcare services, thereby diminishing hospital admissions amidst periods of lockdown [17].

2. Methods

The study was qualitative in methodology conducted in 2024 adhering to the preferred reporting items for systematic reviews guidelines for systematic reviews [18].

2.1. Research Question

The research question was designed as: “What are the outcomes of the utilization of AI within healthcare systems in the Middle East?.”

2.2. Search Methodology

A comprehensive exploration was undertaken across various scholarly databases, including PubMed, Scopus, ProQuest, and the Cochrane database of systematic reviews, with the aim of discerning pertinent literature pertaining to the subject matter. We selected these databases due to their prominence and richness of data within the international stage. The search criteria were delineated into four distinct domains: Outcome, Artificial Intelligence, Health and Middle East. Initially, expansive terms were employed to heighten the search's sensitivity, while synonyms were integrated utilizing the “OR” operator. Subsequently, the “AND” operator was utilized to uphold precision and mitigate the inclusion of irrelevant studies. The outlined search methodology is detailed in Table 1.

Table 1.

Search strategy.

Research question	What are the outcomes of utilization of Artificial Intelligence within the healthcare systems of the Middle East?
Key concepts or terms	Outcome, Artificial Intelligence, Health, Middle East
Databases or sources	Cochrane Library, PubMed, ProQuest, and Scopus.
Time‐period	2000‐2024
#1	outcome* OR Effect OR effects OR result* OR consequence* OR impact OR impacts OR influence OR influences
#2	artificial intelligence OR AI OR machine learning OR deep learning OR Data mining
#3	Health OR Healthcare
#4	Middle‐east* OR Bahrain* OR Cypr* OR Egypt* OR Iran* OR Leban* OR Syria* OR Iraq* OR Palestin* OR Jordan* OR Kuwait* OR Oman* OR Qatar* OR Turk* OR United Arab Emirates OR UAE OR Emirat* OR Yemen*
Final strategy	#1 AND #2 AND #3 AND #4

2.3. Inclusion/Exclusion Criteria

We included studies published in the English language between 2000 and 2024, focusing on the outcomes of AI utilization within healthcare systems in the Middle East. Studies were excluded if they failed to: (1) Address any data pertinent to the study's research question, (2) possess a title or abstract elucidating the research question of the study, or (3) contain a title, abstract, or full‐text that furnished any data concerning the research question of the study.

2.4. Screening and Data Gathering

Following a thorough examination of the studies, pertinent information was extracted using a structured data summarization and collection form. This template included essential details such as publication year, country of origin, study context, study type, AI type, and summary of the study. Summarization forms were diligently completed for each selected article. Subsequently, two researchers reviewed all completed forms and organized them into a table format. Finally, the third researcher contributed insights into any contradictory issues identified within the studies. The form was completed using Microsoft Word software, version 2020.

2.5. Quality Appraisal of Final Studies Using the AACODS Checklist

Two evaluators assessed the quality of all the incorporated studies using the Authority, Accuracy, Coverage, Objectivity, Date, Significance (AACODS) checklist. The AACODS checklist consisted of six questions [19]. During this process, a standardized scoring system was implemented, where a score of “Yes” corresponded to 2, “Can't Tell” to 1, and ‘No’ to 0. These scores ranged from 0 to 12, with higher scores denoting superior quality. The studies were then categorized into one of four groups based on their scores: very low quality (0–3), low quality (4–6), medium quality (7–9), and high quality (10–12). Only studies categorized as medium or high quality were deemed suitable for inclusion in the research endeavors. Any discrepancies between the two authors were resolved through discussion and consultation with a third party acting as an arbitrator. These procedures were repeated twice to ensure consistency.

2.6. Data Analysis

The data extracted from the preceding steps underwent analysis employing a qualitative thematic approach coupled with an inductive methodology. The Boyatzis method for thematic analysis was utilized in this analytical process [20]. The authors systematically reviewed all included studies and extractions to comprehensively comprehend the data, subsequently assigning initial codes to each significant extraction. During the process, a specific code was developed for the data, reflecting a unique outcome of artificial intelligence in healthcare. In the subsequent step, codes that exhibited a common concept were categorized together as sub‐themes. Furthermore, sub‐themes that shared a relatively similar context were consolidated under a single overarching theme. Before categorization into sub‐themes and main themes, all initial codes underwent thorough examination and finalization. Following this, sub‐themes and main themes were designated, elucidated, and organized in a tabular format. Furthermore, to ensure the validity and reliability of the analysis, the authors adhered to the Lincoln and Guba's criteria for qualitative research. These criteria encompassed four fundamental aspects: credibility, transferability, dependability, and confirmability of the qualitative content analysis process [21]. To ensure credibility, the authors conducted rigorous cross‐referencing of the codes with their respective original references on multiple occasions. To establish dependability, a pair of authors conducted the thematic analysis simultaneously and independently to identify any disparities in the results. Confirmability was safeguarded through cross‐examination of the themes, subthemes, and codes by the authors. Lastly, to enhance transferability, the authors ensured that the insights derived from the thematic analysis were expressed in a manner conducive to potential application across various contexts within the healthcare system.

3. Results

3.1. Systematic Review

The findings of the review revealed that among the 373 studies identified from the databases, 8 were found to be duplicates. Following the completion of the screening process, a total of 100 papers were ultimately chosen as the final selection for inclusion in the research (Figure 1). The delineation of the countries wherein the articles were conducted is presented in Figure 2. All of the studies included employed a quantitative approach and utilized machine learning techniques to analyze their data. Appendix 1 (Bibliography) presents further details regarding the characteristics of the included studies.

Figure 1.

Preferred reporting items for systematic reviews diagram of the systematic review [18].

Figure 2.

Countries in which the included studies were conducted.

3.2. Quality Assessment

The quality assessment of the included studies revealed a high level of quality and a low risk of bias. The results of the assessment indicated that the question concerning the dates of the studies received the lowest score, suggesting that majority of the studies might be relatively old. Additional information regarding the results of the quality assessment for the included studies can be found in Appendix 2 (Quality Assessment).

3.3. Thematic Analysis

As delineated in Table 2, the outcomes of the thematic analysis conducted on the data obtained from the included studies were classified into six primary themes. The themes encompassed items including “Prediction of diseases, their diagnosis and outcomes,” “Prediction of organizational issues and attributes,” “Prediction of mental health issues and attributes,” “Prediction of polypharmacy and emotional analysis of the texts,” “Prediction of climate changes issues and attributes,” and “Prediction and identification of healthcare individuals success and satisfaction.” The distribution of each theme among the included studies is illustrated in Figure 3.

Table 2.

Thematic analysis of the data acquired from the included studies.

Theme	Subtheme	Context	Reference
Prediction of diseases, their diagnosis and outcomes	Prediction of diabetes, diabetic nephropathy, ulcers and insulin resistance	Iran, Kuwait, Oman, Egypt	Abdesselam et al. [22, 23, 24, 25, 26, 27, 28, 29]
	Detection of individuals at risk of cancer, its diagnosis, recurrence, metastasis and patient survival	Iran, Turkey, Egypt	Afrash et al. [30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44]
	Prediction of necessity of interventions for brain disorders	Iran	Habibzadeh et al. [45, 46]
	Predicting of fetal, postpartum disorders, newborn birth weight, neonatal mortality and implantation outcomes for individual embryos	Turkey, Oman UAE, Iran	Akbulut et al. [47, 48, 49, 50, 51, 52]
	Prediction of β‐thalassemia	Palestine	AlAgha et al. [53]
	Prediction of multimorbidity	Saudi Arabia, Kuwait	Albagmi et al. [25, 54]
	Prediction of the number of covid‐19 patients, its severity, outcomes and risk mapping	Saudi Arabia, UAE, Egypt, Iran, Bahrain, Kuwait, Qatar, Oman, Iraq,	Alrajhi et al. [55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71]
	Prediction and diagnosis of tuberculosis, brucellosis and other microbiological diseases	Iran, Jordan	Amoori et al. [72, 73, 74, 75, 76, 77]
	Prediction of cardiac and coronary diseases	Bahrain, Kuwait, Oman, UAE, Turkey, Qatar	AlKaabi et al. [25, 78, 79, 80]
	Prediction of systemic lupus erythematosus	Oman	AlShareedah et al. [81]
	Prediction of outcome in trauma patients	Iran	Hassanzadeh et al. [82, 83]
	Prediction of respiratory syndromes spatial‐temporal patterns and their recovery	Saudi Arabia, Iran	John, Shaiba [84, 85]
	Detecting vitamin D status	Cyprus	Sancar, Tabrizi [86]
	Detection of rare thyroid nodules	Turkey	Turk et al. [87]
	Detection of dental and oral disorders	Iran, Cyprus, Turkey	ForouzeshFar et al. [88, 89, 90]
	Prediction of patient‐activation level	Turkey	Demiray et al. [91]
	Automated quality assessment of telehealth services	Middle East	Habib et al. [92]
Prediction of organizational issues and attributes	Optimal Co‐insurance Estimation	Iran	Momahhed et al. [93]
	Forecasting patient demand	Iran	Soltani et al. [94]
	Forecasting Medical Equipment Demand	Turkey	Koç, Türkoğlu [95]
	Optimizing care flows and processes	Egypt	Rashed et al. [96]
	Prediction of work stress	Egypt	Torad et al. [97]
Prediction of mental health issues and attributes	Prediction of Suicide Ideation/Behavior	Middle East	Naghavi et al. [98]
	Prediction of Maternal Depression and Anxiety	Jordan, Palestine, Lebanon, Saudi Arabia, Bahrain	Qasrawi et al. [99]
	Detection of children's mental health and cognitive development	Palestine	Qasrawi et al. [100]
Prediction of polypharmacy and emotional analysis of texts	Prediction of polypharmacy	Iran	Seyedtabib, Kamyari [101]
	Emotional analysis of texts	Saudi Arabia, Iran, Turkey	Alshalan et al. [102, 103, 104, 105]
Prediction of climate change issues and attributes	Prediction of Food Insecurity	Jordan, Palestine, Lebanon, Saudi Arabia, Bahrain	Qasrawi et al. [106]
	Prediction the concentration of nitrogen dioxide (NO2)	UAE, Iran	Al Yammahi, Aung [107, 108, 109]
	Prediction of particulate air pollution	Iran, Iraq, Kuwait	Gholami et al. [110, 111, 112, 113, 114]
	Prediction of heavy metal pollution	Turkey	Günal et al. [115]
	Prediction of salt concentration in water	Iran	Jamei et al. [116]
	Prediction of water pollution level	Iran	Mohammadpour et al. [117]
	Prediction of dust susceptibility	Iran	Razavi‐Termeh et al. [118]
	Estimating soil organic carbon (SOC)	Iran	Zhang et al. [119]
Prediction and identification of healthcare individuals' success and satisfaction	Prediction of healthcare student success	Qatar	Hammoudi Halat et al. [120]
	Identification of key determinants of patient satisfaction	UAE	Simsekler et al. [121]

Figure 3.

Distribution of each theme of the thematic analysis among the included studies.

3.3.1. Prediction of Diseases, Their Diagnosis and Outcomes

Approximately 71% of the included studies addressed this overarching theme, which encompasses various sub‐themes. The sub‐themes included the prediction of diabetes, diabetic nephropathy, ulcers, and insulin resistance; the detection of individuals at risk of cancer, its diagnosis, recurrence, metastasis, and patient survival; the prediction of necessity for interventions concerning brain disorders; forecasting fetal and postpartum disorders, newborn birth weight, neonatal mortality, and implantation outcomes for individual embryos; the prediction of β‐thalassemia; prognostication of multimorbidity; forecasting the number of COVID‐19 patients, its severity, outcomes, and risk mapping; the prediction and diagnosis of tuberculosis, brucellosis, and other microbiological diseases; anticipation of cardiac and coronary diseases; prediction of systemic lupus erythematosus; prognostication of outcomes in trauma patients; prediction of respiratory syndrome spatial‐temporal patterns and their recovery; detection of vitamin D status; identification of rare thyroid nodules; detection of dental and oral disorders; prediction of patient‐activation levels; and automated quality assessment of telehealth services.

3.3.2. Prediction of Organizational Issues and Attributes

Addressed by approximately 5% of the studies, this theme comprised of several sub‐themes including: optimal co‐insurance estimation, forecasting patient demand, forecasting medical equipment demand, optimizing care flows and processes, and prediction of work stress.

3.3.3. Prediction of Mental Health Issues and Attributes

Addressed by approximately 3% of the studies, this theme included multiple subthemes. The sub‐themes included the prediction of suicide ideation/behavior, maternal depression and anxiety, as well as the detection of children's mental health and cognitive development.

3.3.4. Prediction of Polypharmacy and Emotional Analysis of the Texts

This theme was addressed by approximately 5% of the studies. The corresponding sub‐themes for this theme included prediction of polypharmacy, and emotional analysis of the texts.

3.3.5. Prediction of Climate Changes Issues and Attributes

Addressed by approximately 14% of the studies, this theme included several subthemes. The sub‐themes included the prediction of food insecurity, concentration of nitrogen dioxide, particulate air pollution, heavy metal pollution, salt concentration in water, water pollution level, dust susceptibility, and estimating soil organic carbon.

3.3.6. Prediction and Identification of Healthcare Individuals' Success and Satisfaction

This theme addressed by roughly 2% of the studies. The theme comprised of multiple sub‐themes including prediction of healthcare student success, and Identification of key determinants of patient satisfaction.

4. Discussion

As delineated by the findings of the study outlined in the preceding section, the studies conducted in Iran constituted 48% of the total studies, surpassing those conducted in other countries. Meanwhile, Turkey ranked second, with a mere 12% share of the included studies.

The substantial utilization of AI within Iranian literature found within the current study presented an intriguing departure from prior research indicating a moderate rather than high level of familiarity with AI among Iranian healthcare professionals, alongside a moderately favorable disposition towards its application in medicine. Nonetheless, there exists a cautious attitude among Iranian healthcare staff regarding the expanding role of AI in the medical domain. Moreover, it is highlighted that Iranian healthcare professionals identify the enhancement of diagnostic test accuracy, drug interaction identification, and medical test and imaging analysis as the principal areas where AI can be effectively employed in medicine [122]. Another study indicated that Iranian experts regard clinical decision‐making, medical diagnosis, medical procedures, and patient‐centered care as the primary benefits of ChatGPT, a prominent AI model in the healthcare sector. It was further determined that ChatGPT demonstrates the highest level of usefulness in the domains of information and infrastructure as well as information and communication technologies [123].

As delineated in the findings of the study, the outcomes of utilization of AI in healthcare systems of the middle east could be classified into 6 major themes. The results outlined that the thematic domain of ‘prediction of diseases, their diagnosis, and outcomes’ held the predominant share among the themes in terms of citation within the included studies, amounting to 71% of the included studies. Following this, “prediction of climate change issues and attributes” secured the second position with a 14% share, while “prediction and identification of healthcare individuals” success and satisfaction' garnered the lowest citation among the included studies, representing only 2% of the studies.

Multiple studies with the objective of prioritizing areas for the utilization of artificial AI within healthcare services have outlined its potential in enhancing disease diagnosis and treatment recommendations. In this regard, AI's capability to analyze extensive datasets enables the identification of patterns and surpasses human performance in disease diagnosis, treatment selection, and clinical laboratory testing [124, 125]. This implication aligns with the findings of our study, which identified the domain of predicting diseases, their diagnosis, and outcomes as having the highest citation rate among the included studies.

Within the thematic domain of “prediction of diseases, their diagnosis, and outcomes,” the subtheme focused on “prediction of the number of COVID‐19 patients, its severity, outcomes, and risk mapping” emerged as the most significant contributor among the included studies, constituting approximately 17% of the studies. Conversely, the subtheme concerning “detection of individuals at risk of cancer, its diagnosis, recurrence, metastasis, and patient survival” occupied the second position in terms of contribution, comprising 15% of the included studies.

Multiple studies have underscored the noteworthy impact of the COVID‐19 pandemic on the increased utilization of AI within healthcare systems [126, 127, 128]. This phenomenon elucidates the rationale behind the substantial number of studies documented in our research, which reported on the utilization of AI during the COVID‐19 pandemic. In this context, artificial AI has been deployed during the pandemic to mitigate human‐to‐human contact and minimize exposure risk through the assistance of robotics [127]. Additionally, AI has played a role in screening, diagnosing, prognosticating, and treating COVID‐19 cases [128, 129]. Furthermore, AI has the potential to aid in disease surveillance, contact tracing, drug discovery, and providing clinical decision support [130].

The prevailing condition of cancer within the Middle Eastern realm, specifically in the Arab nations, is a matter of substantial apprehension. Recent research indicates that cancer constitutes a prominent public health challenge in the Middle East, where colorectal cancer ranks as the second most prevalent form of cancer and the third leading cause of mortality in the region [131]. This phenomenon elucidates the rationale behind the substantial adoption AI within the healthcare services catering to cancer patients reported by a considerable portion (17%) of the studies included within this paper [55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71]. It underscores the considerable potential of AI in addressing the challenge of cancer in the Middle East, contingent upon the formulation and execution of a meticulous strategic plan.

The Middle East exhibits the highest global prevalence of diabetes, accompanied by the second‐highest rate of increase worldwide. This surge is fueled by factors such as obesity, sedentary lifestyles, urbanization, and inadequate dietary practices [132, 133, 134, 135]. In this context, it is noteworthy that a substantial proportion (8%) of the studies reviewed in this paper documented the effective application of AI in predicting diabetes, diabetic nephropathy, ulcers, and insulin resistance among patients [22, 23, 24, 25, 26, 27, 28, 29]. This discovery also holds significant implications, indicating the potential of AI utilization in enhancing the condition of diabetic patients in Middle Eastern countries.

Another finding reported by our study pertained to the notable outcomes associated with the application of AI in predicting climate change issues and attributes, as reported by 14% of the studies included in this review [100, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119]. The Middle East region is recognized as one of the most susceptible to the repercussions of climate change. It is undergoing a pronounced warming trend characterized by prolonged and hotter summers, heightened occurrence and severity of heatwaves, and a reduction in precipitation and water resources [136, 137]. Consequently, the depiction of successful and noteworthy utilization of AI concerning climate change within this paper constitutes another substantial implication.

5. Limitations and Implications

The study's limitation resided in its exclusive inclusion of English‐language papers, thereby overlooking those composed in the native languages of each respective country. This oversight carries significant implications for healthcare administrators, policymakers, and prospective researchers within the field. This research emphasized the substantial potential of AI in mitigating the challenges posed by cancer and diabetes in the Middle East, subject to the development and implementation of a comprehensive strategic framework. In situations where the healthcare system is strained, such as during a pandemic, AI can provide an effective solution. AI can analyze patient data quickly and accurately, accelerating early diagnosis and treatment processes. Additionally, it enhances the efficiency of healthcare delivery and supports the optimal use of resources. Another implication of the study was the depiction of successful and noteworthy utilization of AI concerning climate change which can be utilized by the beneficiaries. Additionally, a dearth of research exists concerning the effects of AI utilization in nonclinical facets of healthcare service delivery, encompassing areas such as human resource management and economics, which can be an implication for future researchers. Lastly, Due to the limited scope of this study's manuscript, we were unable to address several important issues concerning the challenges and barriers to the utilization of AI in the Middle East. These issues could be suggested as implications for future researchers, who could explore challenges of AI utilization such as ethics, infrastructure, and technical barriers. Addressing these topics could yield valuable insights for stakeholders in the region.

6. Conclusion

This study identified several significant outcomes associated with the utilization of AI within healthcare systems in the Middle East. Among the studies reviewed, Iran demonstrated the highest level of contribution, followed by Turkey. The most frequently reported domains included disease prediction, diagnosis, and subsequent outcomes, while the prediction of climate change issues also emerged as a notable outcome of AI application in healthcare. The findings emphasized the considerable potential of AI in addressing prevalent healthcare challenges in the region, including cancer, diabetes, and climate change. These findings highlighted the significant opportunity for the utilization of AI in healthcare, contingent upon the development of a concrete plan by policymakers and administrators within the region's healthcare systems.

Author Contributions

M.K. conducted the search within the databases, wrote the introduction, results, methods and discussion sections. M.K. and S.M.M. extracted the data and conducted the analysis; S.M.M. consulted with M.K. during each phase of the study. E.K.D.D. and B.S. revised the manuscript.

Ethics Statement

The authors have nothing to report.

Conflicts of Interest

The authors declare no conflicts of interest.

1. Transparency Statement

MK affirms that this manuscript is an honest, accurate, and transparent account of the study being reported; that no important aspects of the study have been omitted; and that any discrepancies from the study as planned (and, if relevant, registered) have been explained.

Supporting information

Supporting information.

Supporting information.

Data Availability Statement

M.K. had full access to all of the data in this study and takes complete responsibility for the integrity of the data and the accuracy of the data analysis. The study data is available through contacting M.K. (Corresponding author).