ArticleCureus2024
Trends and Public Perception of Artificial Intelligence in Medical Imaging: A Social Media Analysis.
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, 1 of them a synthesis that pooled it.
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, 1 synthesis or guideline pooled it.
- Medical undergraduate students' readiness and anxiety toward artificial intelligence: a systematic review and meta-analysis.BMC medical education · 2025Pooled it
- Global Sentiment Toward Health AI at the Dawn of the ChatGPT Era: Empirical Analysis of Twitter (X) Discourse.Journal of medical Internet research · 2026Article
- MRI Waiting Time Disparities in Saudi Public Hospitals: A Six-Year Retrospective Study.Cureus · 2026Article
- Public perceptions of AI in healthcare: a large-scale BERTopic and sentiment analysis of Reddit discussions.Frontiers in public health · 2026Article
- Bridging semantics and clinical fidelity: a section-based assessment of a vision-language model (RadVLM) for chest x-ray report generation.Frontiers in digital health · 2026Article
- Global English-language-dominated discourse on artificial intelligence in healthcare: a three-year longitudinal analysis of the #AIinHealthcare movement on X.Frontiers in digital health · 2026Article
- Development and Examination of the Psychometric Properties of the Social Perception of Artificial Intelligence in Healthcare Scale in the Turkish Context: Evidence From Hatay Province.International journal of public health · 2026Article
- Changes in public perception of artificial intelligence in healthcare after exposure to ChatGPT.NPJ digital medicine · 2025Article
- Artificial intelligence in health care: Assessing impact on professional roles and preparedness among hospital nurse leaders.Digital healthArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The rapid advancement of artificial intelligence (AI) in medical imaging has generated significant interest and debate among healthcare professionals, researchers, and the general public. This study aims to explore trends and public perception of AI in medical imaging by analyzing social media discussions. Using a retrospective content analysis approach, social media posts from X (formerly known as Twitter) and Reddit were collected, covering discussions from 2019 to 2024. A total of 1,022 posts were analyzed after data cleaning, employing both qualitative and quantitative methods to examine sentiment, themes, and keyword frequencies. The sentiment analysis revealed that 55% of the comments expressed positive sentiments towards AI in medical imaging, emphasizing its potential to enhance diagnostic accuracy and efficiency. Neutral sentiments accounted for 35% of the posts, while 10% expressed negative sentiments, primarily focusing on concerns related to job displacement, ethical issues, and data privacy. Thematic analysis identified four primary themes: ethical and privacy concerns, job displacement, trust and reliability, and workflow efficiency. Keyword frequency analysis highlighted significant discussions around AI, imaging, and radiology. The results underscore both the optimism and concerns associated with AI in medical imaging, emphasizing the need for ongoing dialogue among technology developers, healthcare providers, and the public. Addressing ethical and privacy concerns, and integrating AI responsibly into clinical workflows, is crucial for maximizing its benefits and minimizing potential risks. These findings provide valuable insights into public perceptions and inform strategies for the effective and ethical implementation of AI technologies in healthcare.
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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.