ArticleJournal of multidisciplinary healthcare2026
Integrating Artificial Intelligence into Clinical Care: A Cross-Sectional Study to Advance Healthcare in Saudi Arabia.
Article in Journal of multidisciplinary healthcare, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.