ArticleJSES international2025
Artificial intelligence in shoulder arthroplasty: how smart is it?
Article in JSES international, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 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
3 citing papers in PubMed.
- Artificial Intelligence in Arthroplasty: A Comprehensive Structured Critical Review and Descriptive Evidence Map of Validation, Uncertainty, and Workflow Integration.Bioengineering (Basel, Switzerland) · 2026Review
- Artificial intelligence in shoulder arthroplasty: latest concepts and clinical integration.Journal of orthopaedic surgery and research · 2026Review
- Digital Health and Smart Technologies in Shoulder Arthroplasty: Emerging Tools and Clinical Implications.Orthopedic research and reviews · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: The application of artificial intelligence (AI) is growing rapidly in many fields, and the medical field is no exception. The amount of information we collect before, during, and after surgery is growing exponentially, making the opportunity for AI applications to be integrated into our daily practice more and more apparent. Methods: A literature search was conducted in January 2024 using PubMed (MEDLINE), SCOPUS, and EMBASE databases. A critical analysis of the relevant literature on AI technology in shoulder arthroplasty was conducted. Results: In the field of shoulder arthroplasty, recent literature reports the predictive value of AI models in predicting length of hospital stay and health-care costs, predicting clinical outcomes and complications, and identifying implants. By leveraging machine learning before, during, and after surgery, surgeons can make clinical decisions, predict possible problems, estimate resources needed and clinical outcomes, and ultimately personalize care for each patient. Conclusion: AI technology is becoming more and more advanced, especially in the medical field. By leveraging machine learning before, during, and after surgery, surgeons can make clinical decisions, predict possible problems, estimate resources needed and clinical outcomes, and ultimately personalize treatment for each patient. Because this technology is still in its infancy, there are several limitations to bringing it into the real-world clinical setting. However, it is advancing at a rapid pace, and therefore as a shoulder surgeon, you need to understand and be interested in AI technology.
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Identifiers
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.