{"title":"Integrating Artificial Intelligence into Clinical Care: A Cross-Sectional Study to Advance Healthcare in Saudi Arabia.","authors":"Maryam Allayl, Ghareeb Bahari","doi":"10.2147/JMDH.S598736","DOIUrl":null,"url":null,"abstract":"<p><strong>Purpose: </strong>Mounting evidence suggests that artificial intelligence can support the self-management of chronic diseases, including skin conditions, insulin management, and blood pressure control. This study aimed to investigate the potential use of artificial intelligence (AI) in chronic condition management among patients in Saudi Arabia, where the prevalence of such diseases is increasing. Specifically, we assessed AI perception, self-efficacy, and cognitive symptom management; examined their associations with demographic variables, and evaluated the influence of AI perception and self-efficacy on cognitive symptom management.</p><p><strong>Patients and methods: </strong>This study employed a cross-sectional, descriptive-correlational design. Data were collected at a single time point to characterize the sample and explore relationships among variables. A convenience sample of 163 patients with chronic conditions was recruited. A structured questionnaire was used to assess AI perception, self-efficacy, cognitive symptom management, and demographic characteristics. Data were collected between December 2024 and March 2025 and were analyzed using descriptive statistics, Pearson's correlation coefficient, one-way analysis of variance, and multiple regression analysis, as appropriate.</p><p><strong>Results: </strong>The findings revealed that sex significantly influenced AI awareness, indicating a need for targeted outreach, particularly for women who demonstrated lower levels of AI awareness. Additionally, self-efficacy was a significant predictor of better cognitive symptom management (p < 0.01), as participants with higher self-efficacy reported significantly better management of cognitive symptoms and greater engagement in health-promoting behaviors compared to those with lower self-efficacy.</p><p><strong>Conclusion: </strong>Our results highlight that self-efficacy is a key factor in managing cognitive symptoms associated with chronic conditions and underscore the importance of targeted interventions to enhance inclusivity and strengthen individuals' confidence in managing their health. These findings can also inform the development of healthcare programs aimed at empowering patient self-management through AI-based tools.</p>","PeriodicalId":16357,"journal":{"name":"Journal of Multidisciplinary Healthcare","volume":"19 ","pages":"598736"},"PeriodicalIF":2.4000,"publicationDate":"2026-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13127431/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Multidisciplinary Healthcare","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.2147/JMDH.S598736","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2026/1/1 0:00:00","PubModel":"eCollection","JCR":"Q2","JCRName":"HEALTH CARE SCIENCES & SERVICES","Score":null,"Total":0}
引用次数: 0
Abstract
Purpose: Mounting evidence suggests that artificial intelligence can support the self-management of chronic diseases, including skin conditions, insulin management, and blood pressure control. This study aimed to investigate the potential use of artificial intelligence (AI) in chronic condition management among patients in Saudi Arabia, where the prevalence of such diseases is increasing. Specifically, we assessed AI perception, self-efficacy, and cognitive symptom management; examined their associations with demographic variables, and evaluated the influence of AI perception and self-efficacy on cognitive symptom management.
Patients and methods: This study employed a cross-sectional, descriptive-correlational design. Data were collected at a single time point to characterize the sample and explore relationships among variables. A convenience sample of 163 patients with chronic conditions was recruited. A structured questionnaire was used to assess AI perception, self-efficacy, cognitive symptom management, and demographic characteristics. Data were collected between December 2024 and March 2025 and were analyzed using descriptive statistics, Pearson's correlation coefficient, one-way analysis of variance, and multiple regression analysis, as appropriate.
Results: The findings revealed that sex significantly influenced AI awareness, indicating a need for targeted outreach, particularly for women who demonstrated lower levels of AI awareness. Additionally, self-efficacy was a significant predictor of better cognitive symptom management (p < 0.01), as participants with higher self-efficacy reported significantly better management of cognitive symptoms and greater engagement in health-promoting behaviors compared to those with lower self-efficacy.
Conclusion: Our results highlight that self-efficacy is a key factor in managing cognitive symptoms associated with chronic conditions and underscore the importance of targeted interventions to enhance inclusivity and strengthen individuals' confidence in managing their health. These findings can also inform the development of healthcare programs aimed at empowering patient self-management through AI-based tools.
期刊介绍：
The Journal of Multidisciplinary Healthcare (JMDH) aims to represent and publish research in healthcare areas delivered by practitioners of different disciplines. This includes studies and reviews conducted by multidisciplinary teams as well as research which evaluates or reports the results or conduct of such teams or healthcare processes in general. The journal covers a very wide range of areas and we welcome submissions from practitioners at all levels and from all over the world. Good healthcare is not bounded by person, place or time and the journal aims to reflect this. The JMDH is published as an open-access journal to allow this wide range of practical, patient relevant research to be immediately available to practitioners who can access and use it immediately upon publication.
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