ReviewShoulder & elbow2024
Advanced technology in shoulder arthroplasty surgery: Artificial intelligence, extended reality, and robotics.
Review in Shoulder & elbow, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 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
7 citing papers in PubMed.
- Practice habits in board-eligible shoulder and elbow orthopedic surgeons.JSES reviews, reports, and techniques · 2026Article
- Multi-source information fusion using CNN-LSTM-Attention for bone layer recognition in robotic orthopedic grinding.Medical & biological engineering & computing · 2026Article
- Artificial intelligence in shoulder arthroplasty: latest concepts and clinical integration.Journal of orthopaedic surgery and research · 2026Review
- Robotic assisted reverse total shoulder arthroplasty: narrative review and surgical technique of humeral and glenoid preparation.JSES reviews, reports, and techniques · 2026Article
- Robotic-assisted baseplate preparation in primary reverse total shoulder arthroplasty: surgical technique and contemporary review.JSES reviews, reports, and techniques · 2026Review
- Advancing Precision in Shoulder Arthroplasty: Patient-Specific Instrumentation, Navigation, and Emerging Technologies.Clinics in orthopedic surgery · 2025Review
- An overview of artificial intelligence and machine learning in shoulder surgery.Clinics in shoulder and elbow · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The purpose of this review is to provide an overview of the integration of technological advancements in orthopedic shoulder surgery. Recent technological advancements in orthopedic shoulder surgery include predictive analytics, computer-navigated instrumentation for operative planning, extended reality, and robotics. Separately, these advancements provide distinct methodological attempts to improve surgical experiences and outcomes. Together, these technologies can provide orthopedic surgeons with the tools and capabilities to improve patient care and communication in shoulder arthroplasty. From artificial intelligence-generated predictive analytics to extended reality and robotics, technical innovations may lead to improvements in patient education, surgical accuracy, interdisciplinary communication, and outcomes. A comprehensive narrative review was conducted to explore the technological advancements of orthopedic shoulder arthroplasty. Our findings emphasized the impact of these advancements, exemplified by early enhancements in efficacy and safety. However, certain challenges remain, such as a lack of reproducibly improved outcomes and cost considerations. While the reviewed studies indicate hope for improving shoulder arthroplasty, the true cost-effectiveness and applicability remains to be determined, indicating the need for further research.
Indexed as
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.