Today's shoulder and elbow edition examines economic justification for robotic assistance in reverse shoulder arthroplasty, histological predictors of rotator cuff repair failure, machine learning approaches to tear prediction, and geographic differences in total shoulder arthroplasty outcomes. Key findings include the economic challenges of routine robotic adoption, the limited predictive value of muscle histology for repair failure, promising but moderate performance of AI-based tear prediction, and surprisingly favorable outcomes at rural centers with experienced surgeons.
"Is Robotic-Assisted Reverse Shoulder Arthroplasty Economically Justified? A Break-Even Analysis." — Menendez ME et al., J Shoulder Elbow Surg — https://doi.org/10.1016/j.jse.2026.03.010"Characterizing histological fatty accumulation, muscle atrophy, and fibrosis in relation to re-tear and revision after primary rotator cuff repair: a mean 3-year follow-up study." — Ruderman LV et al., JSES Rev Rep Tech — https://doi.org/10.1016/j.xrrt.2026.100702"Machine learning-based prediction of rotator cuff tears using anatomical parameters: a retrospective cohort study." — Hou Z et al., BMC Musculoskelet Disord — https://doi.org/10.1186/s12891-026-09765-2"Revision rates following total shoulder arthroplasty in rural and urban hospitals : an Australian Orthopaedic Association National Joint Replacement Registry analysis of high-volume surgeons." — Dragan Z et al., Bone Jt Open — https://doi.org/10.1302/2633-1462.73.BJO-2025-0299.R1