ArticleJournal of experimental orthopaedics2026
Standardised Transparent Orthopaedic Reporting and Modelling for AI (STORM-AI)-Guidelines for reporting artificial intelligence studies in orthopaedics from the ESSKA AI Working Group.
Article in Journal of experimental orthopaedics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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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
1 citing paper in PubMed.
- Large language models show distinct multidimensional performance profiles and variable reproducibility in consensus-based bone stress injury.Journal of experimental orthopaedics · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Purpose: The rapid growth of Artificial Intelligence (AI) in orthopaedic research has led to inconsistencies in study reporting, hindering evaluation and clinical translation. This initiative aimed to develop the STORM-AI (Standardised Transparent Orthopaedic Reporting and Modelling-AI) guidelines to enhance the transparency, completeness, and quality of reporting for AI studies in orthopaedics. Methods: The ESSKA AI Working Group, a multinational and multidisciplinary team of experts, developed the STORM-AI guidelines through a multi-step consensus process. This involved a comprehensive review of existing AI reporting standards (e.g., CONSORT-AI, STARD-AI and TRIPOD), followed by iterative rounds of drafting, review, and refinement to incorporate orthopaedic-specific considerations. Results: The consensus process resulted in the STORM-AI checklist and an accompanying Explanation and Elaboration (E&E) document. The guidelines provide specific reporting recommendations across all study sections, including study design, data characteristics, model development, performance metrics, ethical considerations and clinical workflow integration. Key areas of emphasis include rigorous validation, clear outcome definition, and error analysis within the orthopaedic context. Conclusion: The STORM-AI guidelines provide a crucial framework for authors, reviewers, and journals to improve the evidence base for AI in orthopaedic care. Widespread adoption is anticipated to foster more robust, reproducible, and clinically valuable innovations, facilitating the responsible integration of AI into orthopaedics. Level of Evidence: Level V.
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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.