Transparency of reporting and methodological conduct of prognostic and diagnostic clinical prediction models developed using machine learning in total shoulder arthroplasty: A systematic review and critical appraisal.

ABSTRACT:

Background

This study aimed to synthesize the applications, performance, and methodological conduct of artificial intelligence (AI) prediction models for total shoulder arthroplasty (TSA) outcomes.

Methods

PUBMED, MEDLINE, EMBASE, and CENTRAL were searched on November 2, 2025 for all articles that utilized AI to develop prognostic or diagnostic prediction models utilizing TSA populations. Methodological quality was assessed using the TRIPOD statement and PROBAST tool.

Results

Twenty-four studies comprising outcomes of 497,365 patients (35.6% female; 69.6 ± 0.9 years) were included. Of these patients, 31.0% underwent rTSA, 29.1% aTSA, and 2.8% hemiarthroplasty. The remaining patients received a mix of aTSA and rTSA, but the exact proportions were not reported in their

SUBMITTER: Shanmugaraj A 

PROVIDER: S-EPMC12846907 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature