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Background and AimsThe rapid expansion of artificial intelligence (AI) within worldwide healthcare systems is occurring at a significant rate. In this context, the Middle East has demonstrated distinctive characteristics in the application of AI within the healthcare sector, particularly shaped by regional policies. This study examined the outcomes resulting from the utilization of AI within healthcare systems in the Middle East.MethodsA systematic review was conducted across several databases, including PubMed, Scopus, ProQuest, and the Cochrane Database of Systematic Reviews in 2024. The quality assessment of the included studies was conducted using the Authority, Accuracy, Coverage, Objectivity, Date, Significance checklist. Following this, a thematic analysis was carried out on the acquired data, adhering to the Boyatzis approach.Results100 papers were included. The quality and bias risk of the included studies were delineated to be within an acceptable range. Multiple themes were derived from the thematic analysis including: “Prediction of diseases, their diagnosis, and outcomes,” “Prediction of organizational issues and attributes,” “Prediction of mental health issues and attributes,” “Prediction of polypharmacy and emotional analysis of texts,” “Prediction of climate change issues and attributes,” and “Prediction and identification of success and satisfaction among healthcare individuals.”ConclusionThe findings emphasized AI's significant potential in addressing prevalent healthcare challenges in the Middle East, such as cancer, diabetes, and climate change. AI has the potential to overhaul the healthcare systems. The findings also highlighted the need for policymakers and administrators to develop a concrete plan to effectively integrate AI into healthcare systems.
Background and AimsThe rapid expansion of artificial intelligence (AI) within worldwide healthcare systems is occurring at a significant rate. In this context, the Middle East has demonstrated distinctive characteristics in the application of AI within the healthcare sector, particularly shaped by regional policies. This study examined the outcomes resulting from the utilization of AI within healthcare systems in the Middle East.MethodsA systematic review was conducted across several databases, including PubMed, Scopus, ProQuest, and the Cochrane Database of Systematic Reviews in 2024. The quality assessment of the included studies was conducted using the Authority, Accuracy, Coverage, Objectivity, Date, Significance checklist. Following this, a thematic analysis was carried out on the acquired data, adhering to the Boyatzis approach.Results100 papers were included. The quality and bias risk of the included studies were delineated to be within an acceptable range. Multiple themes were derived from the thematic analysis including: “Prediction of diseases, their diagnosis, and outcomes,” “Prediction of organizational issues and attributes,” “Prediction of mental health issues and attributes,” “Prediction of polypharmacy and emotional analysis of texts,” “Prediction of climate change issues and attributes,” and “Prediction and identification of success and satisfaction among healthcare individuals.”ConclusionThe findings emphasized AI's significant potential in addressing prevalent healthcare challenges in the Middle East, such as cancer, diabetes, and climate change. AI has the potential to overhaul the healthcare systems. The findings also highlighted the need for policymakers and administrators to develop a concrete plan to effectively integrate AI into healthcare systems.
IntroductionGiven structural barriers, access to services is key for preventing drug‐related harms and managing chronic disease among people who inject drugs (PWID). The Patient Activation Measure (PAM), a validated scale to assess self‐efficacy in navigating one's own health care, was operationalised to improve service utilisation and outcomes but has not been assessed among PWID. We characterised PAM and its association with healthcare and harm reduction utilisation among PWID in the AIDS Linked to IntraVenous Experience cohort in Baltimore.MethodsFrom 2019 to 2020, participants completed surveys on PAM, service utilisation and drug use. We used log‐binomial regression to identify correlates of “Lower” PAM and modelled the association between lower PAM and service utilisation, stratified by recent IDU.ResultsParticipants (n = 351) were primarily male (67%), Black (85%) and 24% reported recent IDU. Lower PAM was significantly more common in those reporting IDU (aPR 1.45; 95% CI 1.03, 2.04), heavy alcohol (aPR 1.77; 95% CI 1.24, 2.51) and marijuana (aPR: 1.70; 95% CI 1.23, 2.36) but less common among women (aPR 0.57; 95% CI 0.38, 0.84) and those living with HIV (APR 0.52; 95% CI 0.35, 0.78). In modelling service utilisation, lower PAM was associated with a lower prevalence of methadone utilisation (aPR 0.27; 95% CI 0.09, 0.84) among those reporting IDU, but a higher prevalence of methadone utilisation (aPR 2.72; 95% CI 1.46, 5.08) among those not reporting IDU, after controlling for correlates of PAM.Discussion and ConclusionPAM‐tailored interventions targeting methadone utilisation warrant consideration but should account for socio‐structural barriers to utilisation and correlates of PAM among PWID.
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