ArticleCureus2024
Medical Doctors' Perceptions of Artificial Intelligence (AI) in Healthcare.
Article in Cureus, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 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
9 citing papers in PubMed.
- Perception and challenges of artificial intelligence (AI) in Emergency Medicine: A multi-country study in Sub-Saharan Africa.African journal of emergency medicine : Revue africaine de la medecine d'urgence · 2026Article
- Optimistic and ambivalent attitudes toward clinical artificial intelligence.Turkish journal of medical sciences · 2026Article
- Navigating the artificial intelligence-informed patient: a cross-sectional study of physicians' perceptions and clinical practices.Frontiers in public health · 2026Article
- Review
- Exploring Medical Doctors' Confidence in Artificial Intelligence: The Role of Specialty, Experience, and Perceived Job Security.Healthcare (Basel, Switzerland) · 2025Article
- Ethical principles across countries: does 'ethical' mean the same everywhere?Frontiers in public health · 2025Review
- Cardiovascular evaluation of athletes ahead of participation.Frontiers in cardiovascular medicine · 2025Article
- Physicians' Knowledge, Perceptions and Use of Large Language Models in Clinical Practice:Sultan Qaboos University medical journal · 2025Article
- Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Introduction With the current exponential expansion of robotics, implants, and imaging technologies, diagnostic processes within the healthcare industry are becoming popular platforms for artificial intelligence (AI) use. Thus, an understanding of physicians' attitudes toward AI and the extent to which medical educators are ready to work with AI is necessary. This research aimed to study doctors' perceptions of AI in healthcare. Methods A web-based questionnaire organized into four sections, namely, demographics, concepts of AI, education in AI, and implementation challenges related to AI, was designed systematically based on a literature search and circulated among medical doctors from various fields. Results Study participants exhibited a lower score toward familiarity with AI. Only 52.12% (74/142) of physicians completed the survey. The greatest challenge associated with the use of AI in therapeutic settings was found to be the degree of autonomy, with a score of 3.56. Among the participants, 67.61% felt that the lack of human supervision was the most important limiting factor in the implementation of AI in clinical practice. However, the participants demonstrated a strong interest in understanding the concepts of AI in the near future. Conclusion This study revealed a low degree of familiarity with AI, highlighting the need for medical schools and hospitals to establish specialized education and training programs for physicians to improve patient outcomes.
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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.