ArticleJMIR formative research2025
Understanding Physician Attitudes Toward AI in Clinical Decision-Making: Cross-Sectional Study.
Article in JMIR formative research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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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.
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Who cites it
3 citing papers in PubMed.
- Determinants of AI Adoption in Saudi Arabian Healthcare Institutions.Healthcare (Basel, Switzerland) · 2026Article
- Assessing Primary Care Physicians' Readiness for AI-Based Adaptive Learning: Perceptions, Barriers, and Learning Needs in Northern Saudi Arabia.Healthcare (Basel, Switzerland) · 2026Article
- Physicians' attitudes and perceptions toward the integration of artificial intelligence into pediatric hematology-oncology in Saudi Arabia.Frontiers in medicine · 2026Article
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Authors and funding
1 author.
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No grant is acknowledged in the PubMed record.
Abstract
Background: The Kingdom of Saudi Arabia (KSA) has made tremendous efforts to promote the adoption of advanced technologies such as artificial intelligence (AI). While the successful adoption of AI is dependent on physician perception, there is a scarcity of data concerning KSA physicians' perception of the technology. Objective: The purpose of this study was to conduct a cross-sectional survey that would provide updated statistics on physicians' attitudes toward AI with a focus on ethical and practical perspectives among physicians licensed in the KSA. Methods: A pilot study was conducted with 10 physicians to enhance the clarity of the survey questions. The pilot was followed by a cross-sectional survey, which was conducted through 25 online, self-administered questionnaires hosted on Qualtrics. A total of 218 physicians filled out the survey. The dataset was then exported into Microsoft Excel and analyzed using descriptive statistics rather than inferential analyses given the exploratory nature of this study and its primary aim to generate updated descriptive insights rather than test specific hypotheses. Results: A total of 201 fully filled surveys, representing 127 (63.2%) female and 74 (36.8%) male physicians with experience ranging from 3 to ≥30 years, were analyzed. Most physicians (n=165, 82.1%) trusted AI-based clinical decision-making, and 76.6% (n=154) believed that the technology improved efficiency in health care delivery. Unfortunately, only 25.9% (n=52) of physicians had used AI in the previous year. Common barriers to AI adoption included lack of training, high implementation costs, and resistance to change, as well as concerns related to privacy, data security, bias in AI-based recommendations, patient autonomy, and liability. Participants recommended training through workshops (n=50, 25%), online courses (n=47, 23.4%), hands-on experience (n=44, 21.9%), and a combination of online courses and hands-on experience (n=17, 8.5%). Conclusions: Physicians who responded to this survey supported AI's use in health care but reported facing financial, ethical, and training barriers, which could be addressed through informed consent and staff training.
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