AI-driven healthcare innovations for enhancing clinical services during mass gatherings (Hajj): task force insights and future directions

Abstract

Background

Due to the high complexity of healthcare during mass gatherings (MG), the integration of Artificial Intelligence (AI) might be crucial. AI can enhance healthcare delivery, improve patient care, optimize resources, and ensure efficient management of the large-scale healthcare demands during Hajj. This paper aims to provide an overview of AI utilization specifically during Hajj and explore the potential role of AI-driven tools in healthcare and clinical services provided to pilgrims.

Methods

A task force was formed and included experts healthcare providers, AI specialists, and members from the Saudi Society for Multidisciplinary Research Development and Education (SCAPE Society), Saudi Critical Care Pharmacy Research (SCAPE) platform, Saudi Society of Clinical Pharmacy (SSCP), policymakers, and frontline healthcare practitioners involved in Hajj. The task force first agreed on the framework and voting system, then organized into teams to draft content for specific domains. Consensus was reached using a voting system requiring over 80% agreement, and all task force members reviewed and finalized the drafts. The selection of AI specialists, policymakers, and frontline healthcare practitioners for the task force was based on their expertise and relevance to healthcare during Hajj.

Results

The task force identified key focus areas: (1) Patient Care: AI tools for predictive analytics, triage, resource management, and virtual healthcare. (2) Healthcare Providers: AI in medical imaging, care delivery, provider-patient communication, and training. (3) Operational Management: AI for healthcare documentation and reducing administrative burden. (4) Healthcare Systems: AI for early detection and automation during Hajj. The task force constructed ten statements to guide future initiatives.

Conclusion

Expanding the role of AI in healthcare during MGs will help optimize healthcare outcomes and utilization. Concerns about AI ethics and data security need to be addressed. Additional data is needed to address the gaps in the literature regarding AI's applicability in healthcare services during MGs.

Background

Hajj is one of the world's largest MGs, where more than two million Muslim pilgrims worldwide perform Hajj tasks annually [1]. Saudi Arabia pledges to serve these pilgrims to be ahead of their needs and fulfill their expectations until they return safely to their countries [2]. The uniqueness of healthcare during the Hajj stems from the diversity in patients' medical conditions, language barriers, cultural traditions, and healthcare providers' varied experiences and practices. Hajj presents one of the most challenging and demanding healthcare environments globally, driven by the rapid turnover of patients, challenges in maintaining follow-up due to the brief duration of the Hajj, and the transfer of some patients to different medical facilities post-Hajj. In collaboration with other related sectors, the Ministry of Health (MoH) in Saudi Arabia is dedicated to providing high-quality clinical services, implementing comprehensive emergency plans, and committed to the ongoing training and professional development of healthcare professionals, equipping them with the necessary skills and knowledge to deliver exceptional patient care. Furthermore, the MoH holds the primary responsibility for the preparation, operation, and management of all hospitals in Makkah and sacred ritual areas (Mina, Muzdalifah, and Arafat) for the expected surge of pilgrims by optimizing operations and resources [3].

Artificial Intelligence (AI) is a transformative technology that encompasses various subsets, including machine learning (ML), deep learning, and natural language processing (NLP). AI has been increasingly applied in healthcare, demonstrating efficacy in diagnostic imaging, predictive analytics, robotic-assisted surgery, and personalized treatment plans. Evidence suggests that AI-driven solutions enhance diagnostic accuracy, improve patient management, and optimize resource allocation, thereby addressing key healthcare challenges [4, 5]. AI applications have shown significant promise in disease management, clinical decision support, and resource optimization [2]. Recent technologies, particularly ML, have the potential to provide vast solutions for delivering the best healthcare for extreme situations like mass gatherings, employing ML module's ability not only to analyze large volumes of complex databases but also to predict future problems and suggest solutions to prevent or solve them. By training on the available databases, deep learning algorithms can provide multiple solutions to predict possible risk factors and determine the need for manpower promptly [6]. AI-driven models have also been used to analyze patient data, predict disease outbreaks, and optimize medication management [3, 7, 8].

The Ministry of Health (MoH) employs AI solutions to enhance clinical services and help healthcare practitioners with diagnostic and predictive medicine, patient records, emergency response protocols, and training programs. AI has been utilized in healthcare and preventive medicine and has shown a potential promise with many success stories [7]. It has been used in diagnostics, medical imaging analysis, personalized medicine treatment planning, and predictive analytics. Furthermore, AI has been utilized in healthcare systems, operational management, and resource management [9]. These success stories display the potential of leveraging AI in healthcare to improve patient outcomes, enhance diagnostic accuracy, and reduce cost.

The Kingdom of Saudi Arabia has introduced advanced AI and 5G-powered smart robots, which use NLP and SR technologies, to streamline patient management, enhance communication across languages, and provide real-time decision support [10, 11]. AI has been utilized in previous Hajj seasons and is currently being harnessed in various aspects of the pilgrimage, particularly in public health surveillance, diagnostics, operational management, and crowd management [4, 5]. Furthermore, AI-driven predictive models have been employed to anticipate disease outbreaks and optimize emergency response strategies [9]. Integrating AI-driven solutions across various sectors will address the unique complexities of this high-pressure environment, enhance overall care and patient outcomes, and position Saudi Arabia as a benchmark for technological innovation used at mass gatherings globally.

The full integration of AI into clinical services during Hajj requires additional research to evaluate the long-term effects of AI-driven interventions, assess their cost-effectiveness, and ensure ethical and regulatory compliance in their application. Addressing these gaps is crucial to maximizing AI's potential in mass gathering healthcare and ensuring sustainable, data-driven decision-making for future Hajj seasons [5, 11]. This paper aims to provide insights from the task force on utilizing AI-driven tools to enhance clinical services during mass gatherings like Hajj. It focuses on innovative AI applications for early prediction, prevention, and optimized care to improve patient outcomes, streamline care delivery, reduce unnecessary interventions, and lower healthcare costs. In addition, highlights the importance of ethical and regulatory frameworks to ensure these technologies' safe and effective implementation.

Methods

A task force was created that involved a group of experts, including multidisciplinary healthcare providers, AI specialists, members of the Saudi Society for Multidisciplinary Research Development and Education, members of the Saudi Critical Care Pharmacy Research (SCAPE) platform, members of the Saudi Society of Clinical Pharmacy (SSCP), policymakers from different sectors and representatives from the Ministry of Health (MoH), and a group of frontline practitioners who engaged in providing healthcare during Hajj. AI specialists were chosen for their knowledge in healthcare-related artificial intelligence, such as predictive analytics and decision support systems. Policymakers were selected based on their roles in healthcare policy and regulation, ensuring alignment with national health priorities. Frontline healthcare practitioners, including physicians, pharmacists, and emergency responders, were chosen for their firsthand experience in managing health crises and public health concerns during Hajj. This multidisciplinary approach ensured a well-rounded team capable of addressing healthcare challenges through technological, regulatory, and practical perspectives.

The task force first convened to agree upon the framework structure and the voting system methodology to be used throughout the process. In instances where disagreements arose among members, the task force agreed to consult an external expert reviewer. This expert would provide impartial insights and recommendations, helping to mediate disputes and enhance the overall integrity of the process. Following this, the task force was organized into groups, each responsible for drafting content for specific domains and providing recommended statements and insights for each section.

The task force adopted a comprehensive approach by reviewing existing AI applications in healthcare and leveraging expert judgment. The team analyzed real-world implementations, case studies, and published research to assess the effectiveness, limitations, and ethical considerations of AI in large-scale health events like Hajj. Simultaneously, expert insights from AI specialists, healthcare providers, and policymakers were integrated to contextualize findings, address gaps, and ensure the proposed recommendations were practical and forward-thinking. This dual approach allowed the task force to develop well-rounded, evidence-based strategies for AI implementation in Hajj healthcare settings.

The task force identified the following domains for this topic: 1) AI tools related to Patient Care, including Utilizing Predictive Analytics to Improve Patient Care and Safety, Streamlining Triage and Resource Management for Enhanced Patient Care and Virtual Healthcare Assistance and Patient Education;2) AI tools related to Healthcare Providers, including Leveraging Artificial Intelligence for Medical Imaging During Hajj Season, AI-Based applications for enhancing care delivery and provider-patient communication and Healthcare Professionals training using AI during mass gatherings; 3) AI tools related to healthcare Operational Management, including AI in Healthcare documentation and AI applications to support administrative burden of the healthcare processes; 4) AI tools related to Healthcare Systems, including AI-based early detection systems and Employing AI and automation in Hajj season. A voting system methodology was employed to achieve consensus on the proposed opinion statements, with consensus defined as agreement from more than 80% of task force members. After reaching a consensus, the drafts were reviewed and evaluated by all members of the task force to finalize and approve the statements for inclusion in the final version.

Results

The task force identified several important areas of focus to facilitate the development of recommendations, as summarized in Table 1.

Table 1.

 Summary for taskforce insightful statements

Domain	Statements
AI tools related to Patient Care	Statement 1: AI-driven predictive models can enhance patient care and optimize medical services for pilgrims. Statement 2: AI-powered algorithms can enhance the quality of patient care and minimize the burden during medical emergencies by streamlining triage and resource management. Statement 3: AI-powered tools can enhance the patient access to healthcare services by providing remote digital services during mass gatherings
AI tools related to Healthcare Providers	Statement 4: Utilizing AI tools for medical imaging during the Hajj season can enhance diagnostic precision and accelerate healthcare delivery. Statement 5: AI-based applications can enhance care delivery and provider-patient communication by enabling real-time data analysis and personalized interactions Statement 6: Incorporating AI-driven training for HCPs during mass gatherings could enhance real-time decision-making, preparedness, and response efficiency
AI tools related to healthcare Operational Management	Statement 7: AI-driven medical documentation and coding in Hajj medical facilities can improve healthcare continuity and Hajj healthcare preparedness. Statement 8: AI-driven resource management can optimize healthcare staffing, supplies, resources allocation to enhance healthcare response and efficiency during the Hajj season.
AI tools related to Healthcare Systems	Statement 9: The use of AI-based early detection systems during Hajj season is encouraged to predict and prevent disease outbreaks and enhance public health safety. Statement 10: Incorporating robotics and AI-based applications can enhance the workflow and efficiency of health care services provided to pilgrims.

Domain 1. AI tools related to patient care

This section will explore how the use of AI in healthcare is rapidly expanding, particularly in mass gatherings (MGs) where medical emergencies are frequent, and patient volume is high [5]. It will inquire into how AI tools, such as predictive analytics, can improve patient care during Hajj and other MGs by forecasting future events and enhancing preparedness [4]. The section will also examine how AI can streamline triage and resource allocation through machine learning algorithms. Additionally, it will explore how AI-powered virtual assistants can provide real-time digital services to reduce healthcare provider workloads and improve patient outcomes [11, 12].

Utilizing AI-driven predictive analytics to improve patient care and safety

Statement 1: AI-driven predictive models can enhance patient care and optimize medical services for pilgrims

Predictive analytics in healthcare is a field that utilizes AI and data modeling techniques to identify high-risk populations and anticipate future events based on current and historical data to prepare and provide optimal patient care proactively [13]. In the context of MGs, predictive models can utilize patient demographics, important clinical indexes, and medical history to anticipate the risk of hospital admissions [13]. A recent observational study analyzing historical records of pilgrims over four decades reported an increase in the temperature in Makkah by 0.2–0.4 Celsius degrees per decade [14]. The study reported a 74.6% reduction in heatstroke due to structural and community mitigation strategies such as environmental engineering and building design, such as water mist sprays in streets and public transport. According to the Saudi MoH, mortaity rates due to heatstroke continue to be a concern in every hajj season [14]. Using AI-driven predictive models could anticipate the likelihood of heat-related illness among pilgrims, identify high-risk populations, and provide adequate preventive measures and appropriate timely interventions and management. Thus, stakeholders and healthcare providers can prepare adequate inventory, medications, medical supplies, and staffing, eventually improving patient care and minimizing the burden during medical emergencies.

The incidence of heat-related illness continues, highlighting the need for more innovative approaches to targeted patient interventions, such as AI and predictive models. In a study that evaluated the use of predictive models, including ambient temperature, to anticipate patient presentation rate and transfer to hospital rate at MGs in Belgium [5]. The model performed very well in predicting patient presentation rate during MGs, highlighting the need for future studies. Therefore, developing and implementing AI-driven predictive models that can anticipate patient presentation rates and hospital admissions based on patient demographics, medical history, time during Hajj, and weather conditions could offer a significant solution to enhance patient care and medical services provided to pilgrims.

A key strategy for the success of AI in clinical services during Hajj is to tackle the critical issues of data availability and quality specific to this setting. A robust national framework is essential to support large-scale AI integration in healthcare during Hajj. This framework should prioritize collecting, curating, and sharing high-quality medical data, such as patient records, diagnoses, treatments, and outcomes while ensuring stringent measures for patient privacy, data representativeness to the Hajj population, and data security. Additionally, it should foster collaboration among healthcare providers, research institutions, and technology companies to create and implement AI-driven healthcare solutions that address the unique challenges of Hajj.

Streamlining triage and resource management for enhanced patient care

Statement 2: AI-powered algorithms can enhance the quality of patient care and minimize the burden during medical emergencies by streamlining triage and resource management

AI applications have also been shown to enhance ED triage efficiency [15]. The use of machine learning (ML) algorithms to develop ED triage models has been evaluated in observational studies. One study reported that using an effective triage model has minimized the workload on medical staff and optimized resource allocation, which enhanced emergency treatment. Furthermore, in a study evaluating the use of novel ML algorithms in patients admitted to the ED with syncope, the algorithm has improved patient care by enhancing the hospital’s resource allocation and anticipating the length of hospital stays [4]. Moreover, a retrospective study of patients with intracranial hemorrhage (ICH) or pulmonary embolism (PE) evaluated whether there was a difference in hospital length of stay before and after implementing AI triage software. The study showed that computer-aided triage and prioritization software was associated with a significant reduction in hospital length of stay [16].

The use of computer-aided triage and prioritization software in an emergent care setting has been associated with improved patient outcomes, reduction in mortality and morbidity [17], and reduction in labor time, as well as an increase in return on investment after implementation [18]. More evidence is needed about the use of these tools in MGs. However, given the large volume of patients admitted to Makkah hospitals during Hajj, integrating AI-powered models and algorithms could enhance patient care. In 2021, Saudi Arabia launched smart bracelets distributed to pilgrims as a pilot program where it is expanding. This technology was used to monitor the health status of pilgrims, including oxygen saturation, pulse, and blood pressure, to ensure quick assistance and arrival to the ED [10]. Data about the effectiveness of this service during Hajj has yet to be available. The evidence is growing on the usefulness of AI-enabled wearable sensors as a key for improving health management and speed of treatment [19]. Therefore, implementing AI-powered tools that can help with ED triaging, resource allocations, and real-time health monitoring has the potential to improve the quality of patient care and minimize the burden during medical emergencies.

Virtual healthcare assistance and patient education

Statement 3: AI-powered tools can enhance patient access to healthcare services by providing remote digital services during mass gatherings

AI-powered virtual assistants can provide digital services by interacting with patients in real-time. They can be programmed to provide immediate support and healthcare-provider interaction, answers to the most common questions, symptoms assessment, appointment scheduling, and patient education [11]. Virtual assistants can provide 24/7 support to patients and reduce the workload of healthcare providers, which is very important given the expected high volume of patients during MGs. A study that evaluated the use of generative AI as a virtual assistant showed that its use has improved overall patient quality and offered cost-saving opportunities [12]. Virtual assistants also display empathy, sympathy, and emotional support, which has been shown to improve patients’ satisfaction [11]. There is limited evidence of these tools in MGs, yet they are much needed given the unique challenges during the Hajj season. Notably, Saudi Arabia’s efforts to implement these services, including telehealth services, are ongoing. The Saudi MoH has launched the first virtual hospital in the region that facilitates access to patients and offers immediate consultations with healthcare providers [20]. It also utilizes AI to prioritize provided services based on urgency and importance.

Nevertheless, its implementation during Hajj and whether it will provide services to pilgrims are still being determined. Furthermore, in 2024, the Saudi MoH launched the Pharminiplatform during Hajj season, which is a digital pharmacist that allows pilgrims to communicate and inquire about their medications [20]. The platform provides drug information sourced from reliable databases. It will enable the patients to quickly identify medication by uploading its image and give access to the information in multiple languages, given the diversity of pilgrims from different countries. It is expected to see more innovative approaches to facilitate access to healthcare services during mass gatherings as AI technology and tools continue to develop.

Domain 2. AI tools related to healthcare providers

This section will explore how healthcare services during the Hajj season require significant resources and manpower to meet the health needs of pilgrims. It will inquire into the scale of healthcare provision, as highlighted by a 2019 MoH report estimating around 31,000 healthcare providers [21]. The section will examine how AI applications can assist in the work and training of healthcare providers to improve the quality of care and allow providers to dedicate more time to patient care.

Leveraging artificial intelligence for medical imaging during Hajj season

Statement 4: Utilizing AI tools for medical imaging during the Hajj season can enhance diagnostic precision and accelerate healthcare delivery

AI revolutionized image analysis and interpretation, where it excels at identifying intricate patterns in medical images and detecting anomalies often invisible to human anatomy, improving the accuracy of diagnoses in complex cases. Examples of commercially available systems are Critical Care Suite, HealthPNX, and HealthCXR [22, 23]. Such technologies can empower healthcare providers to appropriately take care of multiple cases to provide healthcare to pilgrims. According to MOH, the Lunit INSIGHT CXR technology was used during the last two Hajj seasons, which resulted in faster and more accurate decisions when it comes to providing care to pilgrims [24].

AI-Based applications for enhancing care delivery and provider-patient communication

Statement 5: AI-based applications can enhance care delivery and provider-patient communication by enabling real-time data analysis and personalized interactions

Medical chatbots showed promising benefits in enhancing the productivity of healthcare providers with various applications [25]. One is using chatbots by providers to diagnose patients based on their signs and symptoms, or chatbots are used to answer drug information questions. An example of that is a system called Chat Ella, which was built to diagnose common chronic diseases, according to Zhang, et al. This system can accurately forecast chronic diseases based on the symptoms reported by users [26]. The utilization of chatbots for drug information questions was tested by Albogami, et al., and they found a similar response to human pharmacists [27]. With this in mind, many studies reported accurate responses from chatpotsbut the risk of having inaccurate information is there [27–29].

Real-time translation tools powered by AI are invaluable in healthcare, especially in diverse and multilingual environments. Having millions of pilgrims from different countries with diverse languages can add to the burden of taking care of pilgrims’ healthcare. Having AI-empowered immediate translation can help clinicians communicate with patients and provide the best care within the appropriate time. Latif, et al. shared his team’s successful story with Malaysian pilgrims [30]. The proposed framework is easy to integrate with smartphones since it utilizes IOT technology, which can help merge more applications.

In addition, incorporating AI in Remote Patient Monitoring (AI RPM) would fit the goal of taking care of pilgrims since there are multiple locations for them to perform Hajj rituals. There are many AI tools approved in the market, with each serving various purposes. More than 50% of approved AI RPMs are used for cardiovascular monitoring [31]. There is a proposed framework utilizing the Internet of Things (IOT) to observe pilgrims’ health during Hajj season [32]. The proposed framework integrates the use of radio frequency identification (RFID) technology to monitor pilgrims’ vitals through wearable devices and in case something happens, the system is able to identify the exact location of pilgrims and send an ambulance immediately.

Healthcare professionals training using AI during mass gatherings

Statement 6: Incorporating AI-driven training for HCPs during mass gatherings could enhance real-time decision-making, preparedness, and response efficiency

The utilization of AI in healthcare necessitates the knowledge and literacy on the use of AI applications to keep up with the workflow and provide up to date healthcare. According to the fourth international conference on mass gathering medicine held in Saudi Arabia, one of the recommendations was establishing a training program that involved multiple response agencies and stakeholders [33]. R Charow, et al. summarized the literature published about delivering sessions, workshops, or courses to healthcare providers or students [34]. For healthcare providers, training can be focused on using AI or interpreting results from AI and explaining results derived from AI applications. All those 3 domains will enable healthcare providers to make decisions based on the results and interactions of AI platforms/applications. An example of such a training module is utilizing AI power simulation platforms to provide healthcare providers with cases and instantaneous feedback based on their interactions and decisions. By utilizing such modules, healthcare providers will be ready to make faster and better decisions since they are exposed to multiple cases that mimic video case scenarios during Hajj season [35].

Domain 3. AI tools related to healthcare operational management

This section will explore how AI applications and tools can streamline administrative and non-clinical operations during the Hajj season to improve service efficiency and accuracy. It will inquire into how these tools can help divert limited staff time towards more critical clinical tasks. The section will also examine the potential of AI in optimizing processes like medical documentation, coding, billing, claims management, resource allocation, scheduling, supply chain monitoring, and automating population surveillance and administrative tasks [36–39]. Additionally, it will investigate why these applications have not been widely implemented in Saudi Arabia yet, despite their great potential in managing operations during mass gatherings like Hajj.

AI in healthcare documentation

Statement 7: AI-driven medical documentation and coding in Hajj medical facilities can improve healthcare continuity and Hajj healthcare preparedness

Medical documentation and coding benefit significantly from AI applications (e.g., Natural Language Processing tools) that can automate the review of healthcare provider notes, update diagnosis summaries, and assign standardized medical codes with remarkable precision [38, 39]. Accurate medical documentation and coding are essential for ensuring continuity of clinical care for the Hajj population as they move between tiers of primary, secondary, and tertiary care facilities, as needed. Lastly, thorough medical documentation and coding for the disease burden and healthcare services provided during the Hajj season are paramount for creating future healthcare-related preparedness plans.

AI applications to support administrative burden of the healthcare processes

Statement 8: AI-driven resource management can optimize healthcare staffing, supplies, and resources allocation to enhance healthcare response and efficiency during the Hajj season

Resource management in healthcare operations encompasses the administration of human resources (e.g., nursing shifts, surgeons’ availability, first responders), infrastructure resources (e.g., bed capacity, operating rooms), and material resources (e.g., medical equipment, consumables, and pharmaceuticals) [37–40]. AI applications effectively integrate resource management, enabling rapid, dynamic responses to emerging demands, such as increased demand in emergency departments (i.e., in Hajj context: heat-related illnesses, stampedes, or injuries), thereby efficiently reallocating relevant resources where needed [40]. Furthermore, AI can aid the sustainability of the healthcare supply chain during the Hajj season by conducting real-time analyses of material and medications consumption, inventory levels, and demand patterns [37–39]. The continuous, active monitoring of medical inventories enables the timely identification of anticipated shortages and then the prioritization and initiation of ordering or transporting inventories as necessary to ensure the availability of material resources and, consequently, the continuity of clinical services.

Domain 4. AI tools related to healthcare systems

AI-based early detection systems

Statement 9: The use of AI-based early detection systems during Hajj season is encouraged to predict and prevent disease outbreaks and enhance public health safety

During hajj seasons, the risk of spreading communicable diseases is high which necessitates vaccinating pilgrims as primary prevention [41]. Epidemic Intelligence from Open Sources (EIOS) is an initiative initiated by WHO for open-source intelligence for public health decision-making [42]. Employing EIOS during mass gatherings showed positive results with a high probability of capturing warning signals which can help make early decisions and interventions [43]. In addition to that, having early warning systems to detect communicable diseases can help manage health care during mass gathering events like Hajj season [44]. The Saudi Ministry of Health shared their experience with the establishment of Health Early Warning System (HEWS), which is utilized during Hajj season [45]. The utilization of HEWS during the Hajj season ended up in enhancing disease detection and facilitating monitoring which can be reflected in proactive and early interventions [45]. There are several examples where AI is employed in early detection of infectious diseases; examples of such solutions including Metabiota which is a tracker for more than 200 pathogens and EPIWATCH which is a tool utilizing AI to generate an early warning based on international epidemic data [46]. According to the 4th International Mass Gatherings Medicine, the committee recommended implementing EWR systems and employing AI could facilitate the process and enhance the outcome to make wise decisions during this high-stress season [33].

Employing AI and automation in Hajj season

Statement 10: Incorporating robotics and AI-based applications can enhance the workflow and efficiency of healthcare services provided to pilgrims

AI and robotic systems are revolutionizing healthcare by handling routine tasks. This allows healthcare providers to focus more on patient care. For example, robots can precisely dispense medications, cutting down on errors and freeing up pharmacists for more complex duties [47]. According to Khatib, et al., using AI-enhanced robotic systems results in ​cost and error reduction, a decrease in patient waiting time, and increase in service delivery efficiency [48]. Additionally, automated logistics systems can optimize the distribution of medical supplies across Hajj facilities, ensuring the timely availability of essential resources. Al Nemari, et al. shared their successful utilization of robotics in automating the dispensing of medication at King Fahad Medical City, which reduced medication errors and increased time spent in counseling [49]. Such successful experiences could be used to transform healthcare services provided during the Hajj season.

Discussion

To evaluate real-world AI development metrics as the largest worldwide mass-gathering events, the implementation of AI technologies during Hajj represents a pivotal advancement in managing their complexity. Establishing precise metrics to assess the effectiveness of AI recommendations is crucial for upcoming Hajj deployments. These real-world AI deployment metrics include response time reduction, accuracy of predictive models, user adoption rate, operational efficiency, and public safety outcomes. Likewise, crowd flow optimization systems can be evaluated based on their ability to reduce congestion time by a measurable percentage, while AI-driven health surveillance systems should be assessed by their early detection rate of infectious diseases compared to traditional methods.

Consequently, define how each AI recommendation would be measured for success during future hajj implementation. To ensure the effectiveness of AI-driven solutions in healthcare during Hajj initially, seamless integration of AI with existing healthcare infrastructure can be evaluated through system interoperability rates, clinician adoption rates, and frequency of system downtimes during peak periods. High integration success would be reflected in minimal disruption to electronic health records (EHR) workflows and widespread clinician usage. Moreover, there is an urge to assess robust data management practices using data accuracy rates, real-time data availability, and compliance with national and international data security standards. The absence of data breaches and the ability to deliver timely, reliable clinical data will be critical performance indicators in such a high-volume, and time-sensitive setting.

Subsequently, ethical AI deployment must be measured through algorithm bias detection tools, transparency indices, and surveys assessing healthcare provider and pilgrim trust, and double-check each used tool by human-supervised approaches to Ensuring that AI outputs are equitable and explainable across diverse pilgrim populations is essential for maintaining public trust. Furthermore, cultural sensitivity can be quantified through localization coverage (e.g., support for Arabic language, gender-specific services), user engagement metrics, and satisfaction surveys that capture pilgrim perspectives on the relevance and appropriateness of AI tools. Low abandonment rates of AI services will signal positive cultural alignment.

Ultimately, multidisciplinary collaboration, can be tracked by the number of cross-sector partnerships formed as between health authorities, AI developers, and religious institutions, policy compliance audits, and adherence to rollout timelines. Finally, the adaptability and scalability of AI tools must be evaluated by their ability to function efficiently under peak load conditions, the frequency of updates or system refinements, and the reusability of the system in subsequent Hajj or Umrah seasons.

One research utilized MG’s AI program to assess 94 performance indicators from 59 FIFA World Cup matches to forecast results. This study used the ANN algorithm to forecast Qatar World Cup results. Data were standardized at 50% possession. ANN has 75.42% accuracy and 76.96% AUC in six machine learning models. The model has 72.73% accuracy, 65.31% recall, 77.03% specificity, and 68.82% F1 score [50].

Regarding decision-making during previous mass gatherings, a scoping review encompassed ten articles that invesitgate MGs during the COVID-19 pandemic [51]. The events studied were categorized into five types: academic, political, religious, sports-related, and mixed-type MGs [51]. Agent-based simulations model hazardous interactions between pilgrims during the Hajj in religious MG. The results indicated that as the number of pilgrims increased, it was more difficult to maintain physical distancing, lead to that the contact management is key to assessing transmission risks in future events [51].

Malaysia’s response to the COVID-19 pandemic involved the use of DEMATEL (Decision Making Trial and Evaluation Laboratory) and fuzzy rule-based techniques for assessment [51]. The country identified movement control orders, international travel restrictions, and the cancellation of mass gatherings as key factors in preventing COVID-19 transmission [51]. For a nonreligious MG in the Tokyo Olympic Games, transmission scenarios are studied through a model of multiple branching processes, evaluating the potential transmission of COVID-19 during the Tokyo 2020 Olympic Games [51]. Significantly results in reducing cases, underlining the importance of keeping transmission levels below epidemic levels [51]. During the COVID-19 pandemic, Saudi Arabia leveraged AI to ensure a safe Hajj season by introducing electronic health cards, biometric verification devices, and robots for disinfection and contactless water distribution, resulting in zero reported COVID-19 cases during Hajj 2021 [52].

Specifically, through AI and Sustainability, Saudi Arabia employed Agent-Based Modeling (ABM) to simulate crowd behavior and optimize emergency evacuation strategies in the Jamarat stoning area during Hajj [10]. The simulation was developed using NetLogo. Specifically, the model incorporated dynamic decision-making, simulating real-world uncertainties in crowd movement. By using three hazard scenarios (no hazard, fire, and bomb explosion) [10]. As a result, AI simulation revealed that exit placement—not just the number of exits—critically influenced evacuation success [10]. Redesigned exit configurations, informed by simulation output, reduced evacuation time by up to 67% and improved the evacuee rate by over 300% in some scenarios [10]. The model identified high-risk zones especially near the entrance during fire scenarios, enabling better hazard response planning [10].

Disaster reaction With the rise of “smart cities” and “sensor cities,” AI can handle massive digital data streams to improve early warning systems and resilience methods [53]. AI—particularly ML and DL—is increasingly used in pre-, during-, and post-disaster management [53]. AI is crucial for pre-disaster risk prediction and resource allocation [53]. Deep learning models and metaheuristic evolutionary algorithms forecast landslides and floods better than previous approaches [53]. Fuzzy clustering with Monte Carlo simulations, KNN with Bayesian frameworks, logistic regression, and ANNs have been used to predict earthquakes, unstable slopes, and floods [53]. IoT integration improves real-time environmental data collecting for prediction algorithms [53].

During the time of emergencies, AI enhances decision-making and system coordination [53]. DNNs, CNNs, and SVMs are utilized for real-time prediction and monitoring, including CCTV flood forecasts and fire detection [53]. GIS-based decision support systems, NLP for social media analysis, and supervised learning for emergency vehicle routing have proven effective [53]. Wireless sensor networks (WSNs) and social media analytics also contribute to hazard detection and public engagement [53]. Moreover, AI facilitates damage assessment, loss calculation, and humanitarian response coordination after a disaster [53]. Satellite photography is used to estimate human death and infrastructure damage using backpropagation neural networks and object-based classifiers [53]. Rule-based engines and simulations optimize humanitarian relief chains in hybrid decision support systems [53].

In conclusion, it is critical to align AI applications with validated international benchmarks from prior MGs. Metrics such as infection transmission rate reduction, pilgrim satisfaction scores, and reduction in emergency medical response time should be systematically recorded and reported. These benchmarks not only offer a comparative foundation but also promote continuous improvement in the deployment of AI technologies during Hajj.

Future recommendations

The integration of AI-driven solutions in healthcare services during the Hajj season offers immense potential across four key domains including patient care, healthcare providers, operational management, and healthcare systems. Future research and implementation efforts should include clear, structured, and phased implementation action plans for AI adoption to overcome key challenges. In this paper, we propose several short- and long-term action plan implementation strategies to maximize this potential.

Integration of AI systems with existing healthcare infrastructure

The seamless integration of AI systems with existing healthcare infrastructure is crucial for effective deployment. This includes ensuring interoperability between AI tools and current medical technologies. A systematic, action-oriented framework that addresses infrastructure readiness, system compatibility, training programs, validation, and impact assessment processes is essential for the successful integration of AI-driven solutions into Hajj healthcare services. Conducting a comprehensive system audit is the first essential step for effective integration and assessing current digital infrastructure capabilities, data types and formats, and interoperability between AI tools and existing medical technologies [54].

To ensure effective integration, clear AI success metrics should be established to measure performance and inform decision-making. KPIs include increased clinical efficiency (e.g., shorter triage and consultation times), enhanced diagnostic accuracy (e.g., improved sensitivity and specificity), increased clinician adoption, and improved system uptime across integrated platforms. These measures substantiate evidence-based adoption and assist in validating AI’s impact on healthcare delivery [55, 56].

Developing compatible systems that facilitate data sharing and communication among healthcare providers, applications, and devices is essential to maximizing the benefits of AI-driven solutions during Hajj by promoting collaboration among healthcare providers, government bodies, and technology companies [10]. Telemedicine platforms can improve pilgrimage healthcare. AI-driven telemedicine improves triage, facilitates remote consultations, and monitors pilgrims’ health in real-time, reducing the demands on on-site healthcare providers [57]. AI and telemedicine foster collaboration among healthcare professionals, governmental entities, and technology companies, which is crucial for healthcare during Hajj. Successful integration necessitates standardized data formats and comparable technologies. This approach enhances productivity without replacing current technologies, enabling healthcare providers to access and analyze data for improved clinical treatment and decision-making [58]. However, healthcare organizations have significant challenges, including data quality, cybersecurity, and the demand for AI expertise in telemedicine [54, 57]. Pilot projects are recommended to evaluate the integration of AI with telemedicine, providing learning opportunities and minimizing risk.

Ensuring data quality, security, and privacy

Robust data management practices must be implemented to maintain the accuracy and security of patient data, especially during mass gatherings like Hajj, where enormous amonts of health data are generated in a short period of time. High-quality and secure data are critical for the successfully implementing AI-powered healthcare solutions during the Hajj. Healthcare organizations should develop standardized data governance standards and protocols, which include defined policies for data collection, coding, validation, and sharing, to ensure consistent AI outcomes. Adopting international standards, such as SNOMED CT and LOINC, can significantly enhance interoperability and facilitate seamless integration of AI among systems [59, 60].

Action plan strategies include automating data validation, applying AI to identify errors, and conducting periodic checks to maintain data integrity [61]. To protect sensitive health data during Hajj, it is essential to implement privacy and cybersecurity protections, including encryption, restrictive access controls, and privacy impact assessments [2, 62]. Additionally, it is crucial to establish frameworks that address ethical and legal considerations to ensure the use, quality, and accessibility of data [63].

The efficacy and reliability of AI in healthcare during Hajj require clear KPIs that cover both technical performance and clinical impact. Key metrics consist of reduced data processing times, improved detection accuracy via sensitivity and specificity, and adherence to data security protocols. Additional KPIs for system efficacy include the resolution rate of data conflicts post-audit and user satisfaction among clinical staff [64, 65]. This integrated approach assures the validity, safety, and ethical principles of AI systems utilization, which improves health services for pilgrims during Hajj.

Establishing training and educational programs

Establishing well-structured training and educational programs for healthcare practitioners and professionals is crucial for the effective application of AI-driven solutions during Hajj. These programs equip practitioners with the essential skills to utilize AI tools effectively and foster a culture of acceptance and trust in these technologies. Specialized training and educational programs can enhance practical understanding of AI systems and decision-making processes and facilitate seamless integration of AI into healthcare workflows. These training and educational programs should based on scenario-based curricula that include case-based simulations and real-time decision-making scenarios to address the unique challenges of mass gatherings, such as the high volume of pilgrims and cultural sensitivities. Ensuring up-to-date knowledge requires the use of periodic workshops, certification modules, and online learning platforms [34, 66].

Measuring the effectiveness of these training programs includes introducing several practical KPIs of effectiveness, such as staff completing AI training certification, AI-derived solutions post-training adoption rate, clinical decision-making accuracy improvements, decreasing diagnostic mistakes post-training, and user satisfaction and positive feedback [34, 67]. Establishing these training and educational approaches within Hajj preparation strategies will improve workforce capacity and ensure that AI systems are seamlessly integrated into real-time healthcare services during Hajj.

Enhancing cost-efficiency

The cost-effectiveness of AI-derived solutions during Hajj is a key factor for healthcare providers that can improve financial management and facilitate cost reduction. Several metrics and KPIs can be introduced to evaluate the impact of this strategy, including a reduction in unnecessary expenses, a deacrease in emergency admissions by optimizing bed occupancy, waste minimization, and an improvement in resource consumption [68].

Considering ethical, cultural, and collaborative factors

The implementation of AI dururing Hajj has to consider ethical considerations. Ensuring fairness, transparency, and accountability in AI algorithms will be essential to maintaining trust among healthcare providers and pilgrims. Additionally, addressing cultural sensitivities and ensuring that AI tools are aligned with the local context and values will be important for widespread acceptance [63]. Collaborations among healthcare professionals, AI experts, and policymakers are key to navigating these complex issues [69]. AI-driven technologies can contribute to equitable, effective, and culturally aligned healthcare delivery during Hajj by integrating these ethical, cultural, and collaborative factors.

Minimize unintended consequences

AI raises a risk of unintended consequences that could occur. These consequences involve patient safety, privacy, and the limited sustainability of using AI in healthcare. For instance, using AI-based recommendations, particularly in high-pressure and time-sensitive situations such as Hajj, may impact the clinical judgment of healthcare providers, leading to misdiagnoses or the overlooking critical signs. Hence, dealing with AI as a complementing system rather than an essential system in clinical judgment is imperative.

Conclusion

AI-driven solutions have the potential to transform healthcare delivery during the Hajj season, enhancing patient care, improving diagnostic accuracy, and streamlining administrative processes. By focusing on four key domains including patient care, healthcare providers, operational management, and healthcare systems, AI can significantly improve the healthcare experience for millions of pilgrims. However, to ensure sustainable success, it is crucial to address challenges related to system integration, data security, and ethical considerations. Establishing clear and structured strategic actionable plans and measurable performance metrics and KPIs will facilitate evidence-based decision-making and continuous improvement during Hajj. With a focus on responsible AI adoption and interdisciplinary collaboration, AI can drive future improvements in healthcare quality and access, benefiting both pilgrims and broader healthcare systems during mass gatherings.

Abbreviations

AI

Artificial intelligence

MGs

Mass gatherings

MoH

Ministry of health, Saudi Arabia

ED

Emergency department

ML

Machine learning

ICH

Intracranial hemorrhage

PE

Pulmonary embolism

HCPs

Healthcare providers

IoT

Internet of things

RFID

Radio frequency identification

NLP

Natural Language processing

EIOS

Epidemic intelligence from open sources, a WHO initiative

WHO

World health organization

HEWS

Health early warning system, which is utilized by the Saudi MoH

SCAPE

Saudi critical care pharmacy research platform

SSCP

Saudi society of clinical pharmacy

KPIs

Key performance indicators

SNOMED CT

Systematized nomenclature of medicine-clinical terms

LOINC

Logical observation identifiers names and codes

Authors’ contributions

A.A. conceived the study and designed the manuscript. O.A. and A.F.A. conducted the literature review and contributed to the drafting of the manuscript. R.A. and A.K. analyzed data and provided insights on AI applications in healthcare. A.A. and H.A.B. wrote the main manuscript text and developed the key sections on patient care and operational management. A.M.A. and M.A. contributed to the discussions on healthcare systems and ethical considerations. All authors reviewed and approved the final manuscript.

Funding

No funding was secured to perform the present study.

Data availability

The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request.

Declarations

Ethics approval and consent to participate

Not Applicable.

Consent for publication

Not Applicable.

Competing interests

The authors declare no competing interests.