ArticleTranslational medicine @ UniSa2024
Cross-sectional Study on Medical Attitude Towards Artificial Intelligence Use in Fibromyalgia: Insights From the Annual Thinking Lab on Fibromyalgia Syndrome (ATLAS 2024).
Article in Translational medicine @ UniSa, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 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
2 citing papers in PubMed.
- Conditional Acceptance and the Optimism-Knowledge Gap: A Scoping Review of Attitudes and Perceptions of Artificial Intelligence in Healthcare in Italy.Medical sciences (Basel, Switzerland) · 2026Article
- Expert consensus on feasibility and application of automatic pain assessment in routine clinical use.Journal of anesthesia, analgesia and critical care · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: The integration of artificial intelligence (AI) in healthcare has the potential to revolutionize clinical practice, particularly in the management of complex conditions such as fibromyalgia (FM). Despite its promise, the adoption of this technology in practice faces several challenges, including limited knowledge and preparedness among healthcare professionals. Aim: To evaluate the level of knowledge before and after a workshop on AI in FM among clinicians of different disciplines. Methods: A survey was conducted at the end of the lab. An anonymous 21-item questionnaire was administered to participants. Results: This survey (n = 26) revealed that while most had extensive clinical experience and some prior exposure to AI, the majority lacked sufficient knowledge and felt unprepared to integrate AI into FM management. Post-congress, perceptions of AI improved for many, but significant barriers remained, including lack of training, resistance to change, and cost concerns. Key benefits identified were symptom monitoring and decision support. Targeted training and technical support were highlighted as essential for effective AI adoption in clinical practice. Conclusion: Despite a generally positive shift in perception following the congress, many doctors still feel unprepared and lack the necessary knowledge to effectively utilize AI tools. These results underscore the importance of targeted training and support to implement research and facilitate the integration of AI tools in FM and other clinical settings.
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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.