ArticleCureus2024
Utilization of Artificial Intelligence (AI) in Healthcare Decision-Making Processes: Perceptions of Caregivers in Saudi Arabia.
Article in Cureus, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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
5 citing papers in PubMed.
- Nursing Perceptions of the Intended Use of Artificial Intelligence to Prevent Medication Errors: A Qualitative Descriptive Study.Journal of clinical nursing · 2026Article
- Psychometric evaluation of an instrument measuring artificial intelligence utilization in decision-making domains of healthcare organizations.Scientific reports · 2025Article
- Generative artificial intelligence, large language models and ChatGPT in musculoskeletal Oncology: Current applications and future potential.Journal of clinical orthopaedics and trauma · 2025Article
- Knowledge, Attitudes, and Perceptions of Chronic Patients in Saudi Arabia Regarding the Use of Artificial Intelligence to Improve Medication Adherence.Patient preference and adherence · 2025Article
- A Systematic Review of the Outcomes of Utilization of Artificial Intelligence Within the Healthcare Systems of the Middle East: A Thematic Analysis of Findings.Health science reports · 2024Review
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
Background In the evolving landscape of healthcare, artificial intelligence (AI) has emerged as a transformative force, revolutionizing decision-making processes. Through advanced machine learning and data analytics, AI promises precision and personalized treatments, particularly impactful in diagnostics and personalized medicine. Aim and objectives This study aims to investigate the utilization and effectiveness of AI algorithms among healthcare caregivers, focusing on decision-making processes. Objectives include assessing AI adoption prevalence, understanding demographic factors influencing utilization, and evaluating its impact on decision-making dynamics, diagnostics, and personalized medicine. Methods Employing a quantitative cross-sectional approach, an online questionnaire was distributed to 224 healthcare professionals. The survey covered AI familiarity, perceived effectiveness, and potential barriers. Data analysis utilized descriptive statistics and bivariate analyses. Results Seventy-five percent of caregivers reported that they used AI in the decision-making process, with nurses representing a significant majority (50.4%). Bivariate analyses identified correlations between AI utilization and demographic variables, emphasizing its diverse adoption across specialties. Conclusion This study reveals substantial AI adoption, notably among nurses, indicating a transformative shift in decision-making processes. The findings underscore AI's potential in diagnostics and personalized medicine, highlighting the need for targeted interventions and collaborative efforts to address challenges and maximize AI benefits in healthcare.
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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.