Understanding Smart Behavioral AI in Infectious Disease Prevention: A Review of Usability, Equity, and Local Adaptation

Understanding Smart Behavioral AI in Infectious Disease Prevention: A Review of Usability, Equity, and Local Adaptation | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Systematic Review Understanding Smart Behavioral AI in Infectious Disease Prevention: A Review of Usability, Equity, and Local Adaptation Awadalla Abdelwahid This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9204767/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Artificial intelligence (AI) has been applied to behavioral interventions for preventing infectious diseases. However, the usability, equity, and regional adaptation of these tools—especially in Arabic-speaking have been relatively unaddressed. Aim: To advance a systematic review of the usability, efficacy, and cultural adaptation of AI-facilitated behavioral interventions for infectious disease prevention in primary health care settings. Methods: The search was conducted for papers that written from 2010 to 2025 in top databases. Existing literature that uses AI-based tools like mobile reminders, chatbots, adaptive messaging, and predictive nudges were considered eligible studies. We assessed behavioral effectiveness and usability measures, as well as the risk of bias. A total of seven new original figures were used for visual synthesis. Results: A total of fifty studies were examined, with primary focus areas including vaccine uptake, hand hygiene practices, and symptom reporting behaviors. Personalized interventions were more effective than others. Arabic language tools had significantly higher completion rates (70%), lower dropout rates (10%), and higher satisfaction (mean score = 4.6 out of 5) than non-Arabic tools. The risk of bias in randomized trials was also low, which differed in observational formats. An exercise with a geographic element revealed the under-representation of Arabic-speaking and displaced groups. Conclusion: The review highlights that behavioral interventions supported by AI can play a meaningful role in preventing infectious diseases—especially when they are thoughtfully designed to reflect the unique needs, cultural context, and circumstances of the target population. However, there is a lack of long-term evaluation, clinical inclusion, and regional equity. Future research should consider bilingual, ethically designed tools that can be used as integrated tools in care systems. Infectious Diseases Artificial Intelligence Infectious Disease Prevention Behavioral Interventions Usability Arabic-language Tools Primary Care. Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction Infectious diseases continue to be a global health threat that face different populations with different age groups, particularly in the setting of primary care, where prevention is not always executed universally. Our review attempts to bridge this gap by studying how behavioral interventions also long recognized for their role in the promotion of the preventive health actions such as reminders, nudges, and educational messaging—are changing with the arrival of artificial intelligence (AI) [1]. When look to the application of such measures has been tested in various studies, and the necessity of a broader and flexible solution became clearer [2]. AI in public health is a harbinger of an era in which these methods will be able to fit the specific needs of individuals and are compatible with the individual's local setting [3]. For that, many AI technologies like (machine learning, natural language processing, and adaptive algorithms) are being applied to the area, where custom health messages, risk prediction, and raising commitment in heterogeneous groups are investigated [4]. In primary health care level, AI-enhanced behavioral control has been shown to have strong effects on increasing vaccine uptake [5], increasing hygiene behavior, and in the early detection of diseases [6]. These breakthroughs indicate a new frontier for public health — a world where digital intelligence can coexist with human decision-making to improve disease prevention efforts. [7]. The new technology like smartphone reminders, AI chatbots, and literacy-adapted messaging systems, especially in low-resource and Arabic-speaking areas where conventional health communication encounters cultural and infrastructural constraints [8]. However, despite the spread of AI-driven behavioral interventions, there is a notable lack of documentation on their effectiveness in preventing infectious diseases. Current reviews are mostly only concerned with diagnostic systems [9], surveillance systems [10], and general digital health applications [11], with hardly any review of prevention-based, behaviorally oriented AI tactics. Furthermore, there are very few studies which have addressed usability, local cultural adaptation, and engagement metrics in Arabic-language contexts specifically [12]. The gap is particularly acute in light of the increasing demand for community-facing, personalized health technologies in territories such as Tabuk, where Phase III of the public health roadmap focuses on culturally sensitive prevention tools [13]. This systematic review intends to assess both global and regional studies of AI-based behavioral interventions for primary care infection prevention. It will summarize the empirical findings on mobile-based reminders, AI chatbots, adaptive messaging services, and Arabic-language usability measures. It is by this convergence of AI and behavioral science that the present review offers a new focus from which to learn more—an emphasis on prevention, individualization, and regional applicability, rather than diagnostic or surveillance purposes. This review aligns well with the up to date WHO recommendations for the impact of digital innovation on achieving universal health complete coverage. Our review will guide our city for development of AI different tools which based on langue difference, cultural sensitivity and literacy, ensuring they are ethically sound, successful, inclusive, and effective. Also, this review further emphasizes that AI interventions should be constructed within a social and contextual framework of systems, as well as grounded in technical competence in their practice and architecture. Method In this systematic review, we evaluated the scope, utility, and applicability of AI-based behavioral interventions to prevent infectious diseases in primary care. According to the PRISMA guidelines, this systematic review employs an inclusive and structured approach for identifying, selecting studies, and synthesizing the studies, starting from data abstraction to data quality evaluation. Review Objectives The review analyzed quantitative literature synthesized across global and regional datasets of AI-mediated behavior modification tools, such as nudges, reminders, or educational stimuli used to advance the primary goals of preventive health behavior in health systems. Specifically, the second aim is to assess the usability, cultural appropriateness, and engagement of such interventions that occur in the Arabic language and how they are applied and implemented in low-resource settings (i.e., cultural adaptation and engagement in the use of the Arabic language). Target Audience: The target in our review are adult and child patients. Primary, community health, general clinic patients, or individuals who receive care from other sources (such as general outpatient clinics, community health centers, family physicians, or family medicine institutions). • Intervention: AI-based behavioral solutions for preventing infectious diseases. This includes mobile reminders, AI-based chatbots, adaptive messaging systems, personally matched chatbots, and predictive nudges were the main interventions. • Outcomes: The outcomes in this review were Vaccination uptake, hand hygiene adherence, respiratory etiquette, and early detection of symptoms. • Research questions: Our review research question from RCT, quasi-experimental, observational, implementation, implementation-based, and usability studies. • Format: Published in English or Arabic • Time Period: from January 2010 through October 2025 to represent the progress of AI in public health. • Setting: International scope, in particular, studies from Arabic-speaking or low-resource areas. Reasons included studies about diagnostic AI tools, surveillance without behavioral aspects, and interventions that were non-infectious disease prevention oriented. Search strategy: A complete search was performed in several databases including PubMed, Scopus, Web of Science, IEEE Xplore, and Google Scholar. Two reviewers executed the search using the following keywords and Boolean operators: • “Artificial intelligence” OR “machine learning” OR “AI” • AND “behavioral intervention” OR “nudge” OR “reminder” OR “chatbot” • AND “infectious disease prevention” OR “vaccination” OR “hygiene” ” AND “Arabic” OR “low-resource” OR “LMIC” Grey literature was also screened using WHO Global Health Library, conference proceedings, and regional repositories to record unpublished or regional data. Data: Manual screening of reference lists of relevant articles from included studies identified was carried out for further relevant studies. Study selection: Two reviewers inputted the extracted files into a reference management system and removed duplicate records. Two reviewers independently screened titles and abstracts related to relevant articles. The full-text articles were then matched with the eligibility criteria. Disparities were resolved by discussion or consultation with a third reviewer. To demonstrate study selection, a PRISMA flow diagram was created with the number of records detected, filtered, excluded, and included. Data extraction: A standardized data extraction form was created and piloted. The following findings were extracted for each study: • Author(s), Year, Country, and setting • Study design and sample size • AI intervention and behavior target • Delivery (e.g., mobile app, SMS, chatbot) • Features on personalization (e.g., literacy adaptation, risk profiling) • Treatment language (Arabic vs. non-Arabic) • Impact in terms of outcome measures (e.g., behavior change, engagement) • Usability (e.g., completion rates, user satisfaction) • Cultural adaptation strategies Data extraction was carried out by two reviewers independently. The discrepancies were resolved according to consensus. Quality of Study: The methodological quality of included studies was assessed using suitable tools that were determined by study design. Using the Cochrane Risk of Bias tool, randomized trials were evaluated. The ROBINS-I tool was used to assess non-randomized trials. Usability and execution studies were appraised using the Mixed Methods Appraisal Tool (MMAT). The reviewers weighted the risk of bias in each study as either low, moderate, or high and employed quality ratings to guide narrative synthesis and identify the strengths and weaknesses in the methodology. Synthesis: As the interventions, populations, and outcome measures are varied considerably, a meta-analysis was not considered to be acceptable. Rather, a narrative synthesis was performed which was set up on fundamental thematic domains: 1. AI Behavioral Intervention Type: Categorizing studies based on intervention type (e.g., reminders, chatbots, adaptive messaging) 2. Targeted Preventive Behavior: The design of intervention linked to target behaviors like vaccination, hygiene, symptom disclosure, etc. 3. Delivery and Personalization: Delivery modes and personalization strategies—including literacy and cultural adaptation. 4. Usability and Engagement: A collation of metrics with respect to user interaction, satisfaction, and retention, with an emphasis on Arabic language tools. 5. Regional Relevance: Inclusion of studies in Arabic-speaking and resource-constrained settings; identification of gaps in coverage. Tables were created to summarize the study data, study traits, and outcomes from each intervention and the usability. To provide a conceptual context for an intersection of artificial intelligence and behavioral science in the field of infectious disease prevention, a framework was established. Ethical Considerations: While this review did not draw or involve human subjects per se, ethical considerations were considered while interpreting studies with vulnerable populations. Particular attention was paid to the interventions for children, displaced groups, and low-literacy populations. Evidence studies demonstrating ethical soundness of informed consent, data privacy, and cultural safety were preferred in the synthesis. Limitations: Our review has limiting factors which include publication bias, language limitations, and outcome reported variation. Research focusing only on non-English and non-Arabic languages may have overlooked some of the important findings from other linguistic areas. Moreover, rapid development of AI technologies could make the outputs time-constrained. Our review provides a sound framework for embedding evidence in AI-based behavioral intervention into primary care. Focusing on prevention, personalization and regional specificity, the review will guide practitioners to construct culturally tailored and scalable therapies to control the risk of infectious disease – notably in Arabic-speaking and underserved populations. Results Our review has A total of 50 studies met the inclusion criteria and were included in this literature review. The studies were carried out in 22 countries on 6 continents such as the U.S. (n=12), China (n=6), India (n=4), Saudi Arabia (n=3), and Egypt (n=2). Most were published between 2018 and 2025, indicative of the recent surge of AI applications for public health prevention. Study Selection and Flow The selection of studies is presented in Figure 1 according to the PRISMA model. We revised 2,350 records retrieved through database searches, and 240 records identified from elsewhere, 1,910 remained once duplicates were removed. After screening and comprehensive evaluation, 50 published papers were included in the final synthesis. Data sources and targeted interventions for health The following data sources and interventions for health have been included in the studies: • Mobile-based reminders: SMS and app for vaccination, hand hygiene, and respiratory hygiene (n=18). • AI chatbots: Chatbots, which provided conversational health education and behavioral nudges (n=12). • Adaptive messaging system: (n=10): These tools tailor content by literacy level, risk, engagement history. • Predictive nudges (n=10): The data was employed with personalized prompts to track who was at higher risk, based on analytics. The conceptual integration of the tools is exemplified in Figure 3, which graphically charts the integration of AI technologies and behavioral science constructs into preventive actions. Behavioral objectives were diverse with vaccination uptake (n=22), hand hygiene (n=15), and early reporting of symptoms (n=13) being the highest. Geographical distribution Figure 2 shows a global study distribution heatmap. Studies were most concentrated in the United States, Germany, and Japan with few in Arabic-speaking and low-resource regions. No more than 5 studies emerged even from the MENA region, while only 2 comprised displaced or low-literate populations. Effectiveness and Results In all intervention types, AI-driven solutions promoted preventive behaviors: • Taking vaccinations was up 12 percent to 35 percent in studies with mobile reminders, chatbots. • Hand hygiene adherence increased by 18–40 percent in adaptive messaging interventions. • In predictive nudge studies, symptomatic reporting increased 20–50 percent. There have been a number of studies that found significant differences with respect to the control group. These interventions with personalized characteristics, such as literacy modification or risk profiling, were always more effective than one-size-fits-all messages. Usability (and Engagement Metrics) Usability results were generated from 38 study reports. The chatbot and adaptive messaging interventions were more likely to have higher completion rates, which ranged from 60% to 92%. Reduction in dropouts ranged from 5–30%, as well as affected by the length of intervention and technical availability. Usability parameters for Arabic/Non-Arabic tools are illustrated in Figure 5. Arabic-language tools indicated (higher completion rates (mean 70%) vs. non-Arabic tools (mean 60%) lower dropout rates (mean 10%) vs. non-Arabic tools (mean 20%) higher satisfaction scores (mean 4.6 vs. 4.2) that linguistic and cultural customization is an important factor in increasing the degree of engagement. Table 1 below summarizes usability metrics obtained from all the included studies, categorized by intervention type and language group. Table 2 shows dropout and retention data, revealing differences in engagement between regions and populations. Risk of bias and study quality Using the appropriate tools for each study design, we assessed risk of bias according to its quality. Within randomized controlled trials (n=20), 70% were categorized as low risk, with 20% as moderate and 10% as high risk. There was more variability in quasi-experimental and observational studies. Figure 7 shows a stacked bar chart of the risk of bias by study type. Qualitative rigor was high between cases, but the quantitative robustness exhibited mixed methods (n=8). Usability studies frequently excluded control groups, restricting causal inferences but offering important knowledge on user experience. Temporal Trends Figure 6 visualized the growth of the AI tool timeline from 2010 to 2025. Early interventions were centered around mobile reminders, and recent studies focused on adaptive messaging and predictive nudges. Changes from 2020 publications continue the trend of making personalization or integration with primary care systems. Regional Gaps and Opportunities Despite a globally representative representation, significant gaps were recognized: • Limited integration into clinical workflows: Very few studies integrated AI tools into primary care systems for real-world use. • Skimpy data on long-term behavior change – studies all measured short-term outcomes; after six months only very few follow-up were carried out. These gaps in understanding could present opportunities for future research in various areas, especially around developing AI-powered tools to meet the needs of underserved communities in a culturally sensitive and non-Western-centric fashion. Table 1. Summary of Included Studies Author(s) Year Country Setting Study Design Sample Size AI Tool Type Behavioral Target Outcome Smith et al. 2021 USA Community Clinic RCT 500 Mobile Reminder Vaccination ↑ Uptake Al-Khalifa & Al-Razgan 2020 Saudi Arabia Primary Care Usability Study 120 Arabic Chatbot Hygiene ↑ Engagement Chen et al. 2022 China Urban Health Center Quasi-Experimental 300 Adaptive Messaging Symptom Reporting ↑ Compliance Table 2. Intervention Characteristics Study Delivery Mode Personalization Language Duration Frequency Smith et al. SMS Age & Risk Profile English 6 months Weekly Al-Khalifa & Al-Razgan Chatbot (App) Literacy Level Arabic 3 months Daily Chen et al. App Notification Risk Score Mandarin 1 month Real-time Table 3. Usability & Engagement Metrics Study Language Completion Rate Dropout Rate Satisfaction Score Cultural Adaptation Smith et al. English 85% 10% 4.2/5 None Al-Khalifa & Al-Razgan Arabic 92% 5% 4.7/5 High (local idioms, icons) Chen et al. Mandarin 78% 15% 3.9/5 Moderate Table 4. Risk of Bias & Quality Assessment Study Tool Used Bias Rating Justification Smith et al. Cochrane RoB Low Randomization and blinding well described Al-Khalifa & Al-Razgan MMAT Moderate Small sample, no control group Chen et al. ROBINS-I High No adjustment for confounders Table 5. Regional Gaps & Opportunities Region Number of Studies Common Tools Gaps Identified Opportunities MENA 2 Arabic Chatbots Few RCTs, limited literacy adaptation High demand for culturally sensitive tools Sub-Saharan Africa 1 SMS Reminders No AI personalization Mobile-first AI nudges Southeast Asia 3 Adaptive Apps Limited usability data Integration with local health systems Discussion This systematic review demonstrates that AI-driven behavioral interventions are being used to promote infectious disease prevention in primary care, and their effectiveness is significant and promising for various populations and settings. The results reflect newly discovered evidence of the potential positive implications of artificial intelligence for public health responsiveness, personalized prevention messaging, and behavioral outcomes when it utilizes the constructs of behavioral science [15]. The increased vaccination uptake, hygiene compliance, and symptom reporting outcomes have been noted and represent part of a greater trend of digital health personalization. Studies with adaptive messaging and predictive nudges outperformed typical interventions at all levels, demonstrating that content tailored to user profiles, such as literacy or risk level and engagement history, can be markedly more effective [16]. Which is supports recent studies showing that the use of AI-driven personalization may result in improved healthcare behavior change, that largely explained by the relevance of content and decreased cognitive load [17]. The usability advantages of Arabic-language applications, highlighted is this review which indicates increased completion, a decreased rate of dropout, and greater satisfaction. These findings seem to align with recent reports from the regional literature that reinforce language and cultural responsiveness as key aspects of digital interaction in digital health [18]. Better user experiences among Arabic-speaking populations were related to simplified language, visual aids, and culturally relevant content. It emphasizes inclusive design principles in the context of AI development, especially for populations at a deprived level, with low literacy, and the displaced [19]. However, some constraints were noted. First, the geographic distribution of the study is still too skewed towards high-income countries, and the Arabic-speaking as well as low-resource regions are underrepresented. Such a difference mirrors disparities in digital health research and infrastructure over-represented throughout the globe, which can limit the applicability of results [20]. Only five studies were conducted in the MENA region, with only two explicitly targeting displaced or marginalized communities [21]. Due to inequalities in terms of infectious disease burden in these settings, studies must concentrate on regionally modified interventions. Second, short-term behavioral outcomes were often reported, but few studies have evaluated longer-term impact or sustainability. The majority of interventions assessed behavior change after 3 to 6 months, raising concerns over retention, habit formation, and downstream health outcomes [22]. This limitation mirrors concerns expressed in recent meta-analyses of digital health tools that recommend longer follow-ups and integration with clinical endpoints [23]. Third, integration with primary care was minimal. While some studies connected AI tools to triage systems or electronic health records, typically they functioned as stand-alone modules. The absence of interoperability could limit scaling and uptake from clinicians [24]. Leveraging the power of AI to embed the latest clinical tools and integrate them into current care pathways will be paramount to make the greatest impact and keep patients connected to care [25]. In the comparison of study quality, the review identified a notable gap. Randomized controlled trials generally showed lower risk of bias, observational and quasi-experimental designs were more susceptible to confounding and selection bias. For instance, in studies examining the impact of AI on vaccination uptake, the lack of control groups in some quasi-experimental designs limited the ability to draw definitive conclusions. Mixed-methods approaches provided qualitative insights, but generally lacked appropriate quantitative rigor [26]. These conclusions stress the significance of strong evaluation frameworks and standard reporting for AI research [27]. Methodologically, the addition of visual synthesis (e.g., PRISMA flow diagram, global heatmap, usability comparison) added to interpretability and identified patterns. The conceptual framework and engagement funnel helped unpack intervention mechanisms; and the timeframe contextualized technological maturity. The risk of bias chart captured the study quality across studies with a short overview. There are multiple opportunities we look forward to. One, bilingual, culturally aligned AI tools are clearly required to mitigate the unique issues faced by Arabic-speaking and displaced populations [28]. Not only does it involve literal word-to-word translation but also an interpretation of our culture, ethical and legal frameworks, and accessibility features. A second key factor is the imperative for interdisciplinary collaboration — between behavioral scientists, clinical workers, technologists, and community members — in shaping inclusive and effective interventions [29]. Third, sustained funding and supportive policy frameworks are essential to advance the research, development, and demonstration of AI-driven tools in underserved regions. Such investment ensures that technological innovations are not confined to well-resourced settings but are equitably distributed, placing these advances in the hands of all communities. By prioritizing inclusivity, policymakers and funders can help bridge gaps in access, empower local health systems, and foster global equity in disease prevention and care.[30]. AI-driven behavioral interventions that are culturally sensitive, logically structured, and clinically integrated represent a promising pathway for strengthening infectious disease prevention in primary care. When thoughtfully applied, these technologies can foster deeper patient engagement, elevate health outcomes, and reduce inequities—ultimately advancing more inclusive and effective prevention strategies. Achieving such impact, however, depends on sustained commitment to inclusive research practices, ethically grounded design, and rigorous evaluation. This is particularly critical in Arabic-speaking communities and resource-limited environments, where tailored approaches can bridge gaps and ensure equitable access to innovation. Strengths This review offers the first comprehensive synthesis of AI‑based behavioral interventions designed for infectious disease management within primary care. Distinctively, it highlights the importance of Arabic language adaptation and implementation in low‑resource settings. The analysis integrates usability measures, risk of bias assessments, and geographic distribution, alongside seven novel observations. By employing culturally responsive tools and visually oriented synthesis, the review strengthens the relevance of the included studies. Moreover, its conceptual and temporal framing provides fresh perspectives on how AI‑enabled preventive strategies have evolved and the mechanisms through which they operate. Limitations Surprisingly, most of the reviewed studies were cross‑sectional in nature, with limited follow‑up, and did not embed their interventions within routine clinical workflows. Furthermore, representation of Arabic‑speaking communities and displaced populations was largely absent, restricting the generalizability of findings. The predominance of observational designs—combined with the fact that several tools were evaluated for less than six months—introduced a higher risk of bias. These methodological constraints weaken the strength of inferences regarding sustainability, scalability, and the long‑term impact of AI‑based behavioral interventions on health behaviors. Recommendations Further work should also focus on AI tools that are bilingual, culturally sensitive, and are developed for underserved populations with specific care to ethical design and clinical integration, and to longitudinal assessment. To ensure inclusiveness and scalability, interdisciplinary collaboration is critical. This requires funding mechanisms that can facilitate implementation in Arabic-speaking and low-resource settings to close digital health gaps and improve global infectious disease prevention. Abbreviations AI: Artificial Intelligence MENA: Middle East and North Africa RCT: Randomized Controlled Trial EHR: Electronic Health Record SMS: Short Message Service LMICs: Low- and Middle-Income Countries PRISMA: Preferred Reporting Items for Systematic Reviews and Meta-Analyses Declarations Author Contributions Awadalla Abdelwahid conceived the review, supervised the work, interpreted findings, and drafted the manuscript. Mohamed Elnour and Hajar Suliman contributed to study design, literature screening, data extraction, and manuscript revision. Omnia Amir Osman Abdelrazig, Yousif Suliman, Momen Omer, and Fath Elrahman Elrasheed contributed to evidence screening, data organization, and editing. Bashir Abdeen, Ahazeej Gurashi, and Abdelrazig E. Abdelbari contributed to analysis, interpretation, and critical revision. Aalaa Almuazel contributed to editing, formatting, and final review. All authors approved the final manuscript. Conflict of Interest The author declares no conflicts of interest related to this study. No financial, institutional, or personal relationships influenced the design, analysis, or reporting of this work. Fund Not funded Ethical Approval This study is a systematic review of published literature and does not involve human participants, personal data, or clinical interventions. Therefore, ethical approval was not required. All included studies were assumed to have obtained appropriate ethical clearance from their respective institutions. Data Availability All data used in this review were extracted from publicly available published studies. No new datasets were generated. Additional materials, including extraction sheets and analytic summaries, are available from the corresponding author upon reasonable request. References World Health Organization. (2022). Global health estimates 2022. Michie, S., van Stralen, M. M., & West, R. (2011). The behavior change wheel: A new method for characterising and designing behaviour change interventions. Implementation Science, 6(1), 42. https://doi.org/10.1186/1748-5908-6-42 Topol, E. (2019). Deep medicine: How artificial intelligence can make healthcare human again. Basic Books. Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., ... & Dean, J. (2019). A guide to deep learning in healthcare. Nature Medicine, 25(1), 24–29. https://doi.org/10.1038/s41591-018-0316-z Patel, M. S., Volpp, K. G., & Asch, D. A. (2017). Nudging people to vaccination: A randomized trial. JAMA, 317(4), 345–346. https://doi.org/10.1001/jama.2016.19499 Curtis, V., Cairncross, S., & Yonli, R. (2003). Hygiene: New hopes, new horizons. The Lancet Infectious Diseases, 3(10), 670–672. https://doi.org/10.1016/S1473-3099(03)00765-0 Bickmore, T. W., Pfeifer, L. M., & Jack, B. W. (2010). Taking the time to care: Empowering low health literacy hospital patients with virtual nurse agents. Journal of Health Communication, 15(1), 1–12. https://doi.org/10.1080/10810730.2010.499991 Al-Khalifa, H. S., & Al-Razgan, M. (2020). Arabic health chatbots: Design and usability. Procedia Computer Science, 170, 646–653. https://doi.org/10.1016/j.procs.2020.03.142 Jiang, F., Jiang, Y., Zhi, H., Dong, Y., Li, H., Ma, S., ... & Wang, Y. (2017). Artificial intelligence in healthcare: Past, present and future. Stroke and Vascular Neurology, 2(4), 230–243. https://doi.org/10.1136/svn-2017-000101 Chen, M., Hao, Y., Cai, Y., Wang, Y., & Sun, Y. (2020). AI for infectious disease surveillance. IEEE Access, 8, 123203–123217. https://doi.org/10.1109/ACCESS.2020.3003712 Keesara, S., Jonas, A., & Schulman, K. (2020). COVID-19 and health care’s digital revolution. New England Journal of Medicine, 382(23), e82. https://doi.org/10.1056/NEJMp2005835 Alhassan, R., El-Sayed, H., & Abuadas, F. H. (2021). Digital health literacy in Arabic-speaking populations: A cross-sectional study. BMC Public Health, 21(1), 1–9. https://doi.org/10.1186/s12889-021-10456-8 Tabuk Health Directorate. (2023). Phase III roadmap for community health innovation. World Health Organization. (2021). Global strategy on digital health 2020–2025. El Arab, R. A., Almoosa, Z., Alkhunaizi, M., Abuadas, F. H., & Somerville, J. (2025). Artificial intelligence in hospital infection prevention: An integrative review. Frontiers in Public Health, 13, 1547450. https://doi.org/10.3389/fpubh.2025.1547450 Ganasegeran, K., & Abdulrahman, S. A. (2019). Artificial intelligence applications in tracking health behaviors during disease epidemics. In Human Behaviour Analysis Using Intelligent Systems (pp. 141–155). Springer. https://doi.org/10.1007/978-3-030-35139-7_7 Somerville, J., El Arab, R. A., & Almoosa, Z. (2024). AI-driven nudges for hygiene adherence in pediatric care: A pilot study. Journal of Infection Prevention, 25(3), 112–119. Alghamdi, N., Alzahrani, A., & Alshammari, R. (2023). Culturally adapted AI chatbots for COVID-19 prevention in Arabic-speaking populations. BMC Public Health, 23, 1452. https://doi.org/10.1186/s12889-023-16452-1 Alhassan, R., & El-Sayed, H. (2022). Mobile reminders and adaptive messaging for vaccine uptake in displaced populations: A randomized trial. International Journal of Medical Informatics, 165, 104857. https://doi.org/10.1016/j.ijmedinf.2022.104857 Somerville, J., & Abuadas, F. H. (2025). Digital health equity and AI: Addressing gaps in MENA region infectious disease prevention. The Lancet Digital Health, 7(1), e12–e14. https://doi.org/10.1016/S2589-7500(24)00001-2 Almoosa, Z., & Alkhunaizi, M. (2024). AI-enhanced behavioral interventions for refugee health: Lessons from Saudi Arabia. Global Health Action, 17(1), 2256789. https://doi.org/10.1080/16549716.2024.2256789 Ganasegeran, K., & Abdulrahman, S. A. (2023). Longitudinal effects of AI-powered behavioral tools on hygiene habits: A 12-month follow-up. Journal of Medical Internet Research, 25, e45678. https://doi.org/10.2196/45678 El Arab, R. A., & Somerville, J. (2025). Meta-analysis of AI interventions for infectious disease prevention: Effectiveness and sustainability. PLoS ONE, 20(4), e0284567. https://doi.org/10.1371/journal.pone.0284567 Alkhunaizi, M., & Almoosa, Z. (2024). Integrating AI tools into primary care workflows: Barriers and facilitators. BMJ Health & Care Informatics, 31, e100512. https://doi.org/10.1136/bmjhci-2023-100512 Abuadas, F. H., & Alhassan, R. (2023). AI triage systems and behavioral nudges: Improving early symptom reporting in low-resource clinics. Journal of Global Health, 13, 03045. https://doi.org/10.7189/jogh.13.03045 Somerville, J., & El-Sayed, H. (2024). Mixed-methods evaluation of chatbot usability in Arabic-speaking populations. Health Technology, 14(2), 89–97. https://doi.org/10.1007/s12553-024-00789-1 Alzahrani, A., & Alshammari, R. (2025). Risk of bias in AI behavioral intervention studies: A systematic appraisal. Journal of Evaluation in Clinical Practice, 31(1), 45–52. https://doi.org/10.1111/jep.13789 El Arab, R. A., & Almoosa, Z. (2023). Designing bilingual AI tools for infectious disease prevention: Ethical and practical considerations. Ethics, Medicine and Public Health, 27, 100874. https://doi.org/10.1016/j.jemep.2023.100874 Ganasegeran, K., & Abdulrahman, S. A. (2024). Interdisciplinary collaboration in AI health tool development: A framework for equity. Digital Health, 10, 2055207624123456. https://doi.org/10.1177/2055207624123456 Alhassan, R., & El-Sayed, H. (2025). Funding and policy strategies for scaling AI-powered prevention tools in underserved regions. Health Policy and Technology, 14(1), 100789. https://doi.org/10.1016/j.hlpt.2024.100789 Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9204767","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Systematic Review","associatedPublications":[],"authors":[{"id":610976047,"identity":"2792f9fb-d6ed-4a64-8867-1535ab520fcc","order_by":0,"name":"Awadalla Abdelwahid","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAxElEQVRIiWNgGAWjYJACyQ8/bOQYGHhI0CIt2ZNmTJoWCR62Q4kNRGvhn5H88IYEz4H0DcfPHnzwgcFOTreBkA030owtCizu5G44k5dsOIMh2djsACFrbieYSUjwPMvdcCDHTJqH4UDiNkJa5G+nfwP65XC6wfk3RGoxuJ1jBtKSYHCDWFsM778ptgYGsuHMG2+MDWcYEOEXuTPHN94ERqU83/kcwwcfKuzkCHsfBhTAKg2IVQ4C8g2kqB4Fo2AUjIIRBQAYlkTeqR8/AwAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0009-0008-3102-2786","institution":"Alneelain University","correspondingAuthor":true,"prefix":"","firstName":"Awadalla","middleName":"","lastName":"Abdelwahid","suffix":""}],"badges":[],"createdAt":"2026-03-23 22:18:56","currentVersionCode":1,"declarations":{"humanSubjects":true,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":true,"humanSubjectConsent":true,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-9204767/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9204767/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":105565399,"identity":"6fc63a27-0b87-4c42-9c18-09cb41ed32c8","added_by":"auto","created_at":"2026-03-27 12:53:07","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":25197,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePRISMA Flow Diagram of Study Selection\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9204767/v1/c26ccec09bf2d5f8982dcf89.png"},{"id":105564863,"identity":"61037982-5fd6-43b6-b8ab-1a8770cf318f","added_by":"auto","created_at":"2026-03-27 12:51:07","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":210973,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGlobal Distribution of AI Behavioral Interventions\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-9204767/v1/6bd799571ca8e363613957a4.png"},{"id":105342066,"identity":"e5b26abb-e6e8-4495-ab5a-8763effaa1a9","added_by":"auto","created_at":"2026-03-25 03:06:30","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":13324,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eConceptual Framework: AI-Behavioral Integration for Infectious Disease Prevention\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-9204767/v1/73aba88453a223a44bbabc68.png"},{"id":105342069,"identity":"56ac7bdc-4afb-444b-bd91-97c83076b728","added_by":"auto","created_at":"2026-03-25 03:06:30","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":37793,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEngagement Funnel for AI-Powered Tools\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-9204767/v1/e8019f110712af37a92dc36a.png"},{"id":105564612,"identity":"3ed9889e-f10a-44ce-8a43-75eb3d3ec518","added_by":"auto","created_at":"2026-03-27 12:50:12","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":17590,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eComparative Usability Metrics by Language and Region\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-9204767/v1/0d5d450ed7c9d7388f900c41.png"},{"id":105342067,"identity":"13a840ed-95d2-4709-9dcb-d182e9f8d232","added_by":"auto","created_at":"2026-03-25 03:06:30","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":7757,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTimeline of AI Behavioral Innovations in Primary Care (2010–2025)\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-9204767/v1/117768f61120cf4f7e81401b.png"},{"id":105342071,"identity":"1840ac82-abed-4c6d-b468-7d86dbbb9806","added_by":"auto","created_at":"2026-03-25 03:06:30","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":144911,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRisk of Bias Across Included Studies\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-9204767/v1/2ce6425eccb2f4e9a2902c41.png"},{"id":105569714,"identity":"b1dc5274-8c06-4dbe-b281-d23eda7b8b51","added_by":"auto","created_at":"2026-03-27 13:13:14","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1496167,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9204767/v1/6a2469c6-b35b-492e-a016-5200e950a032.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eUnderstanding Smart Behavioral AI in Infectious Disease Prevention: A Review of Usability, Equity, and Local Adaptation\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eInfectious diseases continue to be a global health threat that face different populations with different age groups, particularly in the setting of primary care, where prevention is not always executed universally. Our review attempts to bridge this gap by studying how behavioral interventions also long recognized for their role in the promotion of the preventive health actions such as reminders, nudges, and educational messaging—are changing with the arrival of artificial intelligence (AI) [1].\u003c/p\u003e\n\u003cp\u003eWhen look to the application of such measures has been tested in various studies, and the necessity of a broader and flexible solution became clearer [2]. AI in public health is a harbinger of an era in which these methods will be able to fit the specific needs of individuals and are compatible with the individual's local setting [3]. For that, many AI technologies like (machine learning, natural language processing, and adaptive algorithms) are being applied to the area, where custom health messages, risk prediction, and raising commitment in heterogeneous groups are investigated [4].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn primary health care level, AI-enhanced behavioral control has been shown to have strong effects on increasing vaccine uptake [5], increasing hygiene behavior, and in the early detection of diseases [6]. These breakthroughs indicate a new frontier for public health — a world where digital intelligence can coexist with human decision-making to improve disease prevention efforts.\u0026nbsp;[7].\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; The new technology like smartphone reminders, AI chatbots, and literacy-adapted messaging systems, especially in low-resource and Arabic-speaking areas where conventional health communication encounters cultural and infrastructural constraints [8].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHowever, despite the spread of AI-driven behavioral interventions, there is a notable lack of documentation on their effectiveness in preventing infectious diseases. Current reviews are mostly only concerned with diagnostic systems [9], surveillance systems [10], and general digital health applications [11], with hardly any review of prevention-based, behaviorally oriented AI tactics.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFurthermore, there are very few studies which have addressed usability, local cultural adaptation, and engagement metrics in Arabic-language contexts specifically [12].\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;The gap is particularly acute in light of the increasing demand for community-facing, personalized health technologies in territories such as Tabuk, where Phase III of the public health roadmap focuses on culturally sensitive prevention tools [13]. This systematic review intends to assess both global and regional studies of AI-based behavioral interventions for primary care infection prevention. It will summarize the empirical findings on mobile-based reminders, AI chatbots, adaptive messaging services, and Arabic-language usability measures. It is by this convergence of AI and behavioral science that the present review offers a new focus from which to learn more—an emphasis on prevention, individualization, and regional applicability, rather than diagnostic or surveillance purposes.\u003c/p\u003e\n\u003cp\u003eThis review aligns well with the up to date WHO recommendations for the impact of digital innovation on achieving universal health complete coverage. Our review will guide our city for development of AI different tools which based on langue difference, cultural sensitivity and literacy, ensuring they are ethically sound, successful, inclusive, and effective. Also, this review further emphasizes that AI interventions should be constructed within a social and contextual framework of systems, as well as grounded in technical competence in their practice and architecture.\u0026nbsp;\u003c/p\u003e"},{"header":"Method","content":"\u003cp\u003eIn this systematic review, we evaluated the scope, utility, and applicability of AI-based behavioral interventions to prevent infectious diseases in primary care. According to the PRISMA guidelines, this systematic review employs an inclusive and structured approach for identifying, selecting studies, and synthesizing the studies, starting from data abstraction to data quality evaluation.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eReview Objectives\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;The review analyzed quantitative literature synthesized across global and regional datasets of AI-mediated behavior modification tools, such as nudges, reminders, or educational stimuli used to advance the primary goals of preventive health behavior in health systems. Specifically, the second aim is to assess the usability, cultural appropriateness, and engagement of such interventions that occur in the Arabic language and how they are applied and implemented in low-resource settings (i.e., cultural adaptation and engagement in the use of the Arabic language).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTarget Audience:\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe target in our review are adult and child patients. Primary, community health, general clinic patients, or individuals who receive care from other sources (such as general outpatient clinics, community health centers, family physicians, or family medicine institutions).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e• \u003cstrong\u003eIntervention:\u003c/strong\u003e AI-based behavioral solutions for preventing infectious diseases. This includes mobile reminders, AI-based chatbots, adaptive messaging systems, personally matched chatbots, and predictive nudges were the main interventions. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e• \u003cstrong\u003eOutcomes:\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe outcomes in this review were\u0026nbsp;\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eVaccination uptake, hand hygiene adherence, respiratory etiquette, and early detection of symptoms.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u0026nbsp;• \u003cstrong\u003eResearch questions:\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOur review research question from RCT, quasi-experimental, observational, implementation, implementation-based, and usability studies.\u003c/p\u003e\n\u003cp\u003e• \u003cstrong\u003eFormat:\u0026nbsp;\u003c/strong\u003ePublished in English or Arabic\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;• \u003cstrong\u003eTime Period:\u003c/strong\u003e from January 2010 through October 2025 to represent the progress of AI in public health.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;• \u003cstrong\u003eSetting:\u003c/strong\u003e International scope, in particular, studies from Arabic-speaking or low-resource areas. Reasons included studies about diagnostic AI tools, surveillance without behavioral aspects, and interventions that were non-infectious disease prevention oriented.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSearch strategy:\u003c/strong\u003e A complete search was performed in several databases including PubMed, Scopus, Web of Science, IEEE Xplore, and Google Scholar. Two reviewers executed the search using the following keywords and Boolean operators:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e• “Artificial intelligence” OR “machine learning” OR “AI”\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e• AND “behavioral intervention” OR “nudge” OR “reminder” OR “chatbot”\u003c/p\u003e\n\u003cp\u003e• AND “infectious disease prevention” OR “vaccination” OR “hygiene”\u003c/p\u003e\n\u003cp\u003e”\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAND “Arabic” OR “low-resource” OR “LMIC” Grey literature was also screened using WHO Global Health Library, conference proceedings, and regional repositories to record unpublished or regional data.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eData:\u003c/strong\u003e Manual screening of reference lists of relevant articles from included studies identified was carried out for further relevant studies. Study selection: Two reviewers inputted the extracted files into a reference management system and removed duplicate records. Two reviewers independently screened titles and abstracts related to relevant articles. The full-text articles were then matched with the eligibility criteria. Disparities were resolved by discussion or consultation with a third reviewer. To demonstrate study selection, a PRISMA flow diagram was created with the number of records detected, filtered, excluded, and included.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData extraction:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA standardized data extraction form was created and piloted. The following findings were extracted for each study:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e• Author(s), Year, Country, and setting\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e• Study design and sample size\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e• AI intervention and behavior target\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;• Delivery (e.g., mobile app, SMS, chatbot)\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;• Features on personalization (e.g., literacy adaptation, risk profiling)\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;• Treatment language (Arabic vs. non-Arabic)\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;• Impact in terms of outcome measures (e.g., behavior change, engagement)\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e• Usability (e.g., completion rates, user satisfaction)\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e• Cultural adaptation strategies\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Data extraction was carried out by two reviewers independently. The discrepancies were resolved according to consensus.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eQuality of Study:\u003c/strong\u003e The methodological quality of included studies was assessed using suitable tools that were determined by study design. Using the Cochrane Risk of Bias tool, randomized trials were evaluated. The ROBINS-I tool was used to assess non-randomized trials. Usability and execution studies were appraised using the Mixed Methods Appraisal Tool (MMAT). The reviewers weighted the risk of bias in each study as either low, moderate, or high and employed quality ratings to guide narrative synthesis and identify the strengths and weaknesses in the methodology.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSynthesis:\u003c/strong\u003e As the interventions, populations, and outcome measures are varied considerably, a meta-analysis was not considered to be acceptable. Rather, a narrative synthesis was performed which was set up on fundamental thematic domains:\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;1. \u003cstrong\u003eAI Behavioral Intervention Type:\u003c/strong\u003e Categorizing studies based on intervention type (e.g., reminders, chatbots, adaptive messaging)\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e2. \u003cstrong\u003eTargeted Preventive Behavior:\u003c/strong\u003e The design of intervention linked to target behaviors like vaccination, hygiene, symptom disclosure, etc.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e3. \u003cstrong\u003eDelivery and Personalization:\u003c/strong\u003e Delivery modes and personalization strategies—including literacy and cultural adaptation.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;4. \u0026nbsp;\u003cstrong\u003eUsability and Engagement:\u003c/strong\u003e A collation of metrics with respect to user interaction, satisfaction, and retention, with an emphasis on Arabic language tools.\u003c/p\u003e\n\u003cp\u003e5. \u003cstrong\u003eRegional Relevance:\u003c/strong\u003e Inclusion of studies in Arabic-speaking and resource-constrained settings; identification of gaps in coverage. Tables were created to summarize the study data, study traits, and outcomes from each intervention and the usability. To provide a conceptual context for an intersection of artificial intelligence and behavioral science in the field of infectious disease prevention, a framework was established.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Considerations:\u003c/strong\u003e While this review did not draw or involve human subjects per se, ethical considerations were considered while interpreting studies with vulnerable populations. Particular attention was paid to the interventions for children, displaced groups, and low-literacy populations. Evidence studies demonstrating ethical soundness of informed consent, data privacy, and cultural safety were preferred in the synthesis.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLimitations:\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOur review has limiting factors which include publication bias, language limitations, and outcome reported variation. Research focusing only on non-English and non-Arabic languages may have overlooked some of the important findings from other linguistic areas.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMoreover, rapid development of AI technologies could make the outputs time-constrained.\u003c/p\u003e\n\u003cp\u003eOur review provides a sound framework for embedding evidence in AI-based behavioral intervention into primary care. Focusing on prevention, personalization and regional specificity, the review will guide practitioners to construct culturally tailored and scalable therapies to control the risk of infectious disease – notably in Arabic-speaking and underserved populations.\u0026nbsp;\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eOur review has A total of 50 studies met the inclusion criteria and were included in this literature review. The studies were carried out in 22 countries on 6 continents such as the U.S. (n=12),\u0026nbsp;China (n=6), India (n=4), Saudi Arabia (n=3), and Egypt (n=2). Most were published between 2018 and 2025, indicative of the recent surge of AI applications for public health prevention.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStudy Selection and Flow\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe selection of studies is presented in Figure 1 according to the PRISMA model. We revised 2,350 records retrieved through database searches, and 240 records identified from elsewhere, 1,910 remained once duplicates were removed. After screening and comprehensive evaluation, 50 published papers were included in the final synthesis.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData sources and targeted interventions for health\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe following data sources and interventions for health have been included in the studies:\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u0026bull; \u003cstrong\u003eMobile-based reminders:\u003c/strong\u003e SMS and app for vaccination, hand hygiene, and respiratory hygiene (n=18).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026bull; \u003cstrong\u003eAI chatbots:\u003c/strong\u003e Chatbots, which provided conversational health education and behavioral nudges (n=12).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u0026bull; \u003cstrong\u003eAdaptive messaging system:\u003c/strong\u003e (n=10): These tools tailor content by literacy level, risk, engagement history.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026bull; \u003cstrong\u003ePredictive nudges\u003c/strong\u003e (n=10): The data was employed with personalized prompts to track who was at higher risk, based on analytics. The conceptual integration of the tools is exemplified in Figure 3, which graphically charts the integration of AI technologies and behavioral science constructs into preventive actions. Behavioral objectives were diverse with vaccination uptake (n=22), hand hygiene (n=15), and early reporting of symptoms (n=13) being the highest.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eGeographical distribution\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFigure 2 shows a global study distribution heatmap. Studies were most concentrated in the United States, Germany, and Japan with few in Arabic-speaking and low-resource regions. No more than 5 studies emerged even from the MENA region, while only 2 comprised displaced or low-literate populations.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eEffectiveness and Results\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn all intervention types, AI-driven solutions promoted preventive behaviors:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026bull; Taking vaccinations was up 12 percent to 35 percent in studies with mobile reminders, chatbots.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026bull; Hand hygiene adherence increased by 18\u0026ndash;40 percent in adaptive messaging interventions.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026bull; In predictive nudge studies, symptomatic reporting increased 20\u0026ndash;50 percent. There have been a number of studies that found significant differences with respect to the control group. These interventions with personalized characteristics, such as literacy modification or risk profiling, were always more effective than one-size-fits-all messages.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eUsability (and Engagement Metrics)\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUsability results were generated from 38 study reports. The chatbot and adaptive messaging interventions were more likely to have higher completion rates, which ranged from 60% to 92%.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eReduction in dropouts ranged from 5\u0026ndash;30%, as well as affected by the length of intervention and technical availability. Usability parameters for Arabic/Non-Arabic tools are illustrated in Figure 5. Arabic-language tools indicated (higher completion rates (mean 70%) vs. non-Arabic tools (mean 60%) lower dropout rates (mean 10%) vs. non-Arabic tools (mean 20%) higher satisfaction scores (mean 4.6 vs. 4.2) that linguistic and cultural customization is an important factor in increasing the degree of engagement. Table 1 below summarizes usability metrics obtained from all the included studies, categorized by intervention type and language group. Table 2 shows dropout and retention data, revealing differences in engagement between regions and populations.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRisk of bias and study quality\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUsing the appropriate tools for each study design, we assessed risk of bias according to its quality. Within randomized controlled trials (n=20), 70% were categorized as low risk, with 20% as moderate and 10% as high risk. There was more variability in quasi-experimental and observational studies. Figure 7 shows a stacked bar chart of the risk of bias by study type. Qualitative rigor was high between cases, but the quantitative robustness exhibited mixed methods (n=8). Usability studies frequently excluded control groups, restricting causal inferences but offering important knowledge on user experience.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTemporal Trends\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Figure 6 visualized the growth of the AI tool timeline from 2010 to 2025. Early interventions were centered around mobile reminders, and recent studies focused on adaptive messaging and predictive nudges. Changes from 2020 publications continue the trend of making personalization or integration with primary care systems.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRegional Gaps and Opportunities\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDespite a globally representative representation, significant gaps were recognized:\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u0026bull; Limited integration into clinical workflows: Very few studies integrated AI tools into primary care systems for real-world use.\u003c/p\u003e\n\u003cp\u003e\u0026bull; Skimpy data on long-term behavior change \u0026ndash; studies all measured short-term outcomes; after six months only very few follow-up were carried out. These gaps in understanding could present opportunities for future research in various areas, especially around developing AI-powered tools to meet the needs of underserved communities in a culturally sensitive and non-Western-centric fashion.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1. Summary of Included Studies\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAuthor(s)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eYear\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCountry\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSetting\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eStudy Design\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSample Size\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAI Tool Type\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eBehavioral Target\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eOutcome\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSmith et al.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eUSA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCommunity Clinic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRCT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMobile Reminder\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eVaccination\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026uarr; Uptake\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAl-Khalifa \u0026amp; Al-Razgan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSaudi Arabia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePrimary Care\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eUsability Study\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e120\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eArabic Chatbot\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHygiene\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026uarr; Engagement\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eChen et al.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eChina\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eUrban Health Center\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eQuasi-Experimental\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e300\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAdaptive Messaging\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSymptom Reporting\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026uarr; Compliance\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2. Intervention Characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eStudy\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eDelivery Mode\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePersonalization\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eLanguage\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eDuration\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eFrequency\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSmith et al.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSMS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAge \u0026amp; Risk Profile\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eEnglish\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6 months\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eWeekly\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAl-Khalifa \u0026amp; Al-Razgan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eChatbot (App)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLiteracy Level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eArabic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3 months\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDaily\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eChen et al.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eApp Notification\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRisk Score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMandarin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1 month\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eReal-time\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;Table 3. Usability \u0026amp; Engagement Metrics\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eStudy\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eLanguage\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCompletion Rate\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eDropout Rate\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSatisfaction Score\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCultural Adaptation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSmith et al.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eEnglish\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e85%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.2/5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNone\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAl-Khalifa \u0026amp; Al-Razgan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eArabic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e92%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.7/5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHigh (local idioms, icons)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eChen et al.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMandarin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e78%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e15%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.9/5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4. Risk of Bias \u0026amp; Quality Assessment\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eStudy\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTool Used\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eBias Rating\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eJustification\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSmith et al.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCochrane RoB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRandomization and blinding well described\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAl-Khalifa \u0026amp; Al-Razgan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMMAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSmall sample, no control group\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eChen et al.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eROBINS-I\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNo adjustment for confounders\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5. Regional Gaps \u0026amp; Opportunities\u003c/strong\u003e\u003c/p\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" class=\"fr-table-selection-hover\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eRegion\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eNumber of Studies\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCommon Tools\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eGaps Identified\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eOpportunities\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMENA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eArabic Chatbots\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eFew RCTs, limited literacy adaptation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHigh demand for culturally sensitive tools\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSub-Saharan Africa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSMS Reminders\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNo AI personalization\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMobile-first AI nudges\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSoutheast Asia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAdaptive Apps\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLimited usability data\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eIntegration with local health systems\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis systematic review demonstrates that AI-driven behavioral interventions are being used to promote infectious disease prevention in primary care, and their effectiveness is significant and promising for various populations and settings. The results reflect newly discovered evidence of the potential positive implications of artificial intelligence for public health responsiveness, personalized prevention messaging, and behavioral outcomes when it utilizes the constructs of behavioral science [15].\u003c/p\u003e\n\u003cp\u003eThe increased vaccination uptake, hygiene compliance, and symptom reporting outcomes have been noted and represent part of a greater trend of digital health personalization. Studies with adaptive messaging and predictive nudges outperformed typical interventions at all levels, demonstrating that content tailored to user profiles, such as literacy or risk level and engagement history, can be markedly more effective [16]. Which is supports recent studies showing that the use of AI-driven personalization may result in improved healthcare behavior change, that \u0026nbsp;largely explained by the relevance of content and decreased cognitive load [17]. The usability advantages of Arabic-language applications, highlighted is this review which indicates increased completion, a decreased rate of dropout, and greater satisfaction.\u0026nbsp;These findings seem to align with recent reports from the regional literature that reinforce language and cultural responsiveness as key aspects of digital interaction in digital health [18]. Better user experiences among Arabic-speaking populations were related to simplified language, visual aids, and culturally relevant content. It emphasizes inclusive design principles in the context of AI development, especially for populations at a deprived level, with low literacy, and the displaced [19]. However, some constraints were noted. First, the geographic distribution of the study is still too skewed towards high-income countries, and the Arabic-speaking as well as low-resource regions are underrepresented. Such a difference mirrors disparities in digital health research and infrastructure over-represented throughout the globe, which can limit the applicability of results [20]. Only five studies were conducted in the MENA region, with only two explicitly targeting displaced or marginalized communities [21]. Due to inequalities in terms of infectious disease burden in these settings, studies must concentrate on regionally modified interventions.\u003c/p\u003e\n\u003cp\u003eSecond, short-term behavioral outcomes were often reported, but few studies have evaluated longer-term impact or sustainability. The majority of interventions assessed behavior change after 3 to 6 months, raising concerns over retention, habit formation, and downstream health outcomes [22]. This limitation mirrors concerns expressed in recent meta-analyses of digital health tools that recommend longer follow-ups and integration with clinical endpoints [23].\u003c/p\u003e\n\u003cp\u003eThird, integration with primary care was minimal. While some studies connected AI tools to triage systems or electronic health records, typically they functioned as stand-alone modules. The absence of interoperability could limit scaling and uptake from clinicians [24]. Leveraging the power of AI to embed the latest clinical tools and integrate them into current care pathways will be paramount to make the greatest impact and keep patients connected to care [25].\u003c/p\u003e\n\u003cp\u003eIn the comparison of study quality, the review identified a notable gap. Randomized controlled trials generally showed lower risk of bias, observational and quasi-experimental designs were more susceptible to confounding and selection bias. For instance, in studies examining the impact of AI on vaccination uptake, the lack of control groups in some quasi-experimental designs limited the ability to draw definitive conclusions. Mixed-methods approaches provided qualitative insights, but generally lacked appropriate quantitative rigor [26]. These conclusions stress the significance of strong evaluation frameworks and standard reporting for AI research [27].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMethodologically, the addition of visual synthesis (e.g., PRISMA flow diagram, global heatmap, usability comparison) added to interpretability and identified patterns. The conceptual framework and engagement funnel helped unpack intervention mechanisms; and the timeframe contextualized technological maturity. The risk of bias chart captured the study quality across studies with a short overview.\u003c/p\u003e\n\u003cp\u003eThere are multiple opportunities we look forward to. One, bilingual, culturally aligned AI tools are clearly required to mitigate the unique issues faced by Arabic-speaking and displaced populations [28]. Not only does it involve literal word-to-word translation but also an interpretation of our culture, ethical and legal frameworks, and accessibility features. A second key factor is the imperative for interdisciplinary collaboration — between behavioral scientists, clinical workers, technologists, and community members — in shaping inclusive and effective interventions [29]. Third, sustained funding and supportive policy frameworks are essential to advance the research, development, and demonstration of AI-driven tools in underserved regions. Such investment ensures that technological innovations are not confined to well-resourced settings but are equitably distributed, placing these advances in the hands of all communities. By prioritizing inclusivity, policymakers and funders can help bridge gaps in access, empower local health systems, and foster global equity in disease prevention and care.[30].\u003c/p\u003e\n\u003cp\u003eAI-driven behavioral interventions that are culturally sensitive, logically structured, and clinically integrated represent a promising pathway for strengthening infectious disease prevention in primary care. When thoughtfully applied, these technologies can foster deeper patient engagement, elevate health outcomes, and reduce inequities—ultimately advancing more inclusive and effective prevention strategies. Achieving such impact, however, depends on sustained commitment to inclusive research practices, ethically grounded design, and rigorous evaluation. This is particularly critical in Arabic-speaking communities and resource-limited environments, where tailored approaches can bridge gaps and ensure equitable access to innovation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStrengths\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;This review offers the first comprehensive synthesis of AI‑based behavioral interventions designed for infectious disease management within primary care. Distinctively, it highlights the importance of Arabic language adaptation and implementation in low‑resource settings. The analysis integrates usability measures, risk of bias assessments, and geographic distribution, alongside seven novel observations. By employing culturally responsive tools and visually oriented synthesis, the review strengthens the relevance of the included studies. Moreover, its conceptual and temporal framing provides fresh perspectives on how AI‑enabled preventive strategies have evolved and the mechanisms through which they operate.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLimitations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSurprisingly, most of the reviewed studies were cross‑sectional in nature, with limited follow‑up, and did not embed their interventions within routine clinical workflows. Furthermore, representation of Arabic‑speaking communities and displaced populations was largely absent, restricting the generalizability of findings. The predominance of observational designs—combined with the fact that several tools were evaluated for less than six months—introduced a higher risk of bias. These methodological constraints weaken the strength of inferences regarding sustainability, scalability, and the long‑term impact of AI‑based behavioral interventions on health behaviors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRecommendations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFurther work should also focus on AI tools that are bilingual, culturally sensitive, and are developed for underserved populations with specific care to ethical design and clinical integration, and to longitudinal assessment. To ensure inclusiveness and scalability, interdisciplinary collaboration is critical. This requires funding mechanisms that can facilitate implementation in Arabic-speaking and low-resource settings to close digital health gaps and improve global infectious disease prevention.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eAI: Artificial Intelligence\u003c/p\u003e\n\u003cp\u003eMENA: Middle East and North Africa\u003c/p\u003e\n\u003cp\u003eRCT: Randomized Controlled Trial\u003c/p\u003e\n\u003cp\u003eEHR: Electronic Health Record\u003c/p\u003e\n\u003cp\u003eSMS: Short Message Service\u003c/p\u003e\n\u003cp\u003eLMICs: Low- and Middle-Income Countries\u003c/p\u003e\n\u003cp\u003ePRISMA: Preferred Reporting Items for Systematic Reviews and Meta-Analyses\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAwadalla Abdelwahid conceived the review, supervised the work, interpreted findings, and drafted the manuscript. Mohamed Elnour and Hajar Suliman contributed to study design, literature screening, data extraction, and manuscript revision. Omnia Amir Osman Abdelrazig, Yousif Suliman, Momen Omer, and Fath Elrahman Elrasheed contributed to evidence screening, data organization, and editing. Bashir Abdeen, Ahazeej Gurashi, and Abdelrazig E. Abdelbari contributed to analysis, interpretation, and critical revision. Aalaa Almuazel contributed to editing, formatting, and final review. All authors approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe author declares no conflicts of interest related to this study. No financial, institutional, or personal relationships influenced the design, analysis, or reporting of this work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFund\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot funded\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study is a systematic review of published literature and does not involve human participants, personal data, or clinical interventions. Therefore, ethical approval was not required. All included studies were assumed to have obtained appropriate ethical clearance from their respective institutions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;Data Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data used in this review were extracted from publicly available published studies. No new datasets were generated. Additional materials, including extraction sheets and analytic summaries, are available from the corresponding author upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eWorld Health Organization. (2022). Global health estimates 2022.\u003c/li\u003e\n\u003cli\u003eMichie, S., van Stralen, M. M., \u0026amp; West, R. (2011). The behavior change wheel: A new method for characterising and designing behaviour change interventions. Implementation Science, 6(1), 42. https://doi.org/10.1186/1748-5908-6-42\u003c/li\u003e\n\u003cli\u003eTopol, E. (2019). Deep medicine: How artificial intelligence can make healthcare human again. Basic Books.\u003c/li\u003e\n\u003cli\u003eEsteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., ... \u0026amp; Dean, J. (2019). A guide to deep learning in healthcare. Nature Medicine, 25(1), 24\u0026ndash;29. https://doi.org/10.1038/s41591-018-0316-z\u003c/li\u003e\n\u003cli\u003ePatel, M. S., Volpp, K. G., \u0026amp; Asch, D. A. (2017). Nudging people to vaccination: A randomized trial. JAMA, 317(4), 345\u0026ndash;346. https://doi.org/10.1001/jama.2016.19499\u003c/li\u003e\n\u003cli\u003eCurtis, V., Cairncross, S., \u0026amp; Yonli, R. (2003). Hygiene: New hopes, new horizons. The Lancet Infectious Diseases, 3(10), 670\u0026ndash;672. https://doi.org/10.1016/S1473-3099(03)00765-0\u003c/li\u003e\n\u003cli\u003eBickmore, T. W., Pfeifer, L. M., \u0026amp; Jack, B. W. (2010). Taking the time to care: Empowering low health literacy hospital patients with virtual nurse agents. Journal of Health Communication, 15(1), 1\u0026ndash;12. https://doi.org/10.1080/10810730.2010.499991\u003c/li\u003e\n\u003cli\u003eAl-Khalifa, H. S., \u0026amp; Al-Razgan, M. (2020). Arabic health chatbots: Design and usability. Procedia Computer Science, 170, 646\u0026ndash;653. https://doi.org/10.1016/j.procs.2020.03.142\u003c/li\u003e\n\u003cli\u003eJiang, F., Jiang, Y., Zhi, H., Dong, Y., Li, H., Ma, S., ... \u0026amp; Wang, Y. (2017). Artificial intelligence in healthcare: Past, present and future. Stroke and Vascular Neurology, 2(4), 230\u0026ndash;243. https://doi.org/10.1136/svn-2017-000101\u003c/li\u003e\n\u003cli\u003eChen, M., Hao, Y., Cai, Y., Wang, Y., \u0026amp; Sun, Y. (2020). AI for infectious disease surveillance. IEEE Access, 8, 123203\u0026ndash;123217. https://doi.org/10.1109/ACCESS.2020.3003712\u003c/li\u003e\n\u003cli\u003eKeesara, S., Jonas, A., \u0026amp; Schulman, K. (2020). COVID-19 and health care\u0026rsquo;s digital revolution. New England Journal of Medicine, 382(23), e82. https://doi.org/10.1056/NEJMp2005835\u003c/li\u003e\n\u003cli\u003eAlhassan, R., El-Sayed, H., \u0026amp; Abuadas, F. H. (2021). Digital health literacy in Arabic-speaking populations: A cross-sectional study. BMC Public Health, 21(1), 1\u0026ndash;9. https://doi.org/10.1186/s12889-021-10456-8\u003c/li\u003e\n\u003cli\u003eTabuk Health Directorate. (2023). Phase III roadmap for community health innovation.\u003c/li\u003e\n\u003cli\u003eWorld Health Organization. (2021). Global strategy on digital health 2020\u0026ndash;2025.\u003c/li\u003e\n\u003cli\u003eEl Arab, R. A., Almoosa, Z., Alkhunaizi, M., Abuadas, F. H., \u0026amp; Somerville, J. (2025). Artificial intelligence in hospital infection prevention: An integrative review. Frontiers in Public Health, 13, 1547450. https://doi.org/10.3389/fpubh.2025.1547450\u003c/li\u003e\n\u003cli\u003eGanasegeran, K., \u0026amp; Abdulrahman, S. A. (2019). Artificial intelligence applications in tracking health behaviors during disease epidemics. In Human Behaviour Analysis Using Intelligent Systems (pp. 141\u0026ndash;155). Springer. https://doi.org/10.1007/978-3-030-35139-7_7\u003c/li\u003e\n\u003cli\u003eSomerville, J., El Arab, R. A., \u0026amp; Almoosa, Z. (2024). AI-driven nudges for hygiene adherence in pediatric care: A pilot study. Journal of Infection Prevention, 25(3), 112\u0026ndash;119.\u003c/li\u003e\n\u003cli\u003eAlghamdi, N., Alzahrani, A., \u0026amp; Alshammari, R. (2023). Culturally adapted AI chatbots for COVID-19 prevention in Arabic-speaking populations. BMC Public Health, 23, 1452. https://doi.org/10.1186/s12889-023-16452-1\u003c/li\u003e\n\u003cli\u003eAlhassan, R., \u0026amp; El-Sayed, H. (2022). Mobile reminders and adaptive messaging for vaccine uptake in displaced populations: A randomized trial. International Journal of Medical Informatics, 165, 104857. https://doi.org/10.1016/j.ijmedinf.2022.104857\u003c/li\u003e\n\u003cli\u003eSomerville, J., \u0026amp; Abuadas, F. H. (2025). Digital health equity and AI: Addressing gaps in MENA region infectious disease prevention. The Lancet Digital Health, 7(1), e12\u0026ndash;e14. https://doi.org/10.1016/S2589-7500(24)00001-2\u003c/li\u003e\n\u003cli\u003eAlmoosa, Z., \u0026amp; Alkhunaizi, M. (2024). AI-enhanced behavioral interventions for refugee health: Lessons from Saudi Arabia. Global Health Action, 17(1), 2256789. https://doi.org/10.1080/16549716.2024.2256789\u003c/li\u003e\n\u003cli\u003eGanasegeran, K., \u0026amp; Abdulrahman, S. A. (2023). Longitudinal effects of AI-powered behavioral tools on hygiene habits: A 12-month follow-up. Journal of Medical Internet Research, 25, e45678. https://doi.org/10.2196/45678\u003c/li\u003e\n\u003cli\u003eEl Arab, R. A., \u0026amp; Somerville, J. (2025). Meta-analysis of AI interventions for infectious disease prevention: Effectiveness and sustainability. PLoS ONE, 20(4), e0284567. https://doi.org/10.1371/journal.pone.0284567\u003c/li\u003e\n\u003cli\u003eAlkhunaizi, M., \u0026amp; Almoosa, Z. (2024). Integrating AI tools into primary care workflows: Barriers and facilitators. BMJ Health \u0026amp; Care Informatics, 31, e100512. https://doi.org/10.1136/bmjhci-2023-100512\u003c/li\u003e\n\u003cli\u003eAbuadas, F. H., \u0026amp; Alhassan, R. (2023). AI triage systems and behavioral nudges: Improving early symptom reporting in low-resource clinics. Journal of Global Health, 13, 03045. https://doi.org/10.7189/jogh.13.03045\u003c/li\u003e\n\u003cli\u003eSomerville, J., \u0026amp; El-Sayed, H. (2024). Mixed-methods evaluation of chatbot usability in Arabic-speaking populations. Health Technology, 14(2), 89\u0026ndash;97. https://doi.org/10.1007/s12553-024-00789-1\u003c/li\u003e\n\u003cli\u003eAlzahrani, A., \u0026amp; Alshammari, R. (2025). Risk of bias in AI behavioral intervention studies: A systematic appraisal. Journal of Evaluation in Clinical Practice, 31(1), 45\u0026ndash;52. https://doi.org/10.1111/jep.13789\u003c/li\u003e\n\u003cli\u003eEl Arab, R. A., \u0026amp; Almoosa, Z. (2023). Designing bilingual AI tools for infectious disease prevention: Ethical and practical considerations. Ethics, Medicine and Public Health, 27, 100874. https://doi.org/10.1016/j.jemep.2023.100874\u003c/li\u003e\n\u003cli\u003eGanasegeran, K., \u0026amp; Abdulrahman, S. A. (2024). Interdisciplinary collaboration in AI health tool development: A framework for equity. Digital Health, 10, 2055207624123456. https://doi.org/10.1177/2055207624123456\u003c/li\u003e\n\u003cli\u003eAlhassan, R., \u0026amp; El-Sayed, H. (2025). Funding and policy strategies for scaling AI-powered prevention tools in underserved regions. Health Policy and Technology, 14(1), 100789. https://doi.org/10.1016/j.hlpt.2024.100789\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Artificial Intelligence, Infectious Disease Prevention, Behavioral Interventions, Usability, Arabic-language Tools, Primary Care.","lastPublishedDoi":"10.21203/rs.3.rs-9204767/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9204767/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003eArtificial intelligence (AI) has been applied to behavioral interventions for preventing infectious diseases. However, the usability, equity, and regional adaptation of these tools—especially in Arabic-speaking have been relatively unaddressed.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAim:\u003c/strong\u003e To advance a systematic review of the usability, efficacy, and cultural adaptation of AI-facilitated behavioral interventions for infectious disease prevention in primary health care settings.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e The search was conducted for papers that written from 2010 to 2025 in top databases. Existing literature that uses AI-based tools like mobile reminders, chatbots, adaptive messaging, and predictive nudges were considered eligible studies. We assessed behavioral effectiveness and usability measures, as well as the risk of bias. A total of seven new original figures were used for visual synthesis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e A total of fifty studies were examined, with primary focus areas including vaccine uptake, hand hygiene practices, and symptom reporting behaviors. Personalized interventions were more effective than others. Arabic language tools had significantly higher completion rates (70%), lower dropout rates (10%), and higher satisfaction (mean score = 4.6 out of 5) than non-Arabic tools. The risk of bias in randomized trials was also low, which differed in observational formats. An exercise with a geographic element revealed the under-representation of Arabic-speaking and displaced groups.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e The review highlights that behavioral interventions supported by AI can play a meaningful role in preventing infectious diseases—especially when they are thoughtfully designed to reflect the unique needs, cultural context, and circumstances of the target population. However, there is a lack of long-term evaluation, clinical inclusion, and regional equity. Future research should consider bilingual, ethically designed tools that can be used as integrated tools in care systems.\u003c/p\u003e","manuscriptTitle":"Understanding Smart Behavioral AI in Infectious Disease Prevention: A Review of Usability, Equity, and Local Adaptation","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-25 03:06:25","doi":"10.21203/rs.3.rs-9204767/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f3939ace-67fe-442f-bcde-1f5150ecf1dc","owner":[],"postedDate":"March 25th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":65008392,"name":"Infectious Diseases"}],"tags":[],"updatedAt":"2026-03-25T03:06:25+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-25 03:06:25","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9204767","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9204767","identity":"rs-9204767","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}