A practical guide to the implementation of AI in orthopaedic research-Part 4: Prerequisites for a successful orthopedics AI-driven project in terms of interdisciplinary collaboration, data management, ethical approval and technology

Umile Giuseppe Longo; Mario Merone; Emiliano Schena; Benedetta Bandini; Guido Nicodemi; Bálint Zsidai; Ann Sophie Hilkert; Eric Hamrin Senorski; Alberto Grassi; Christophe Ley; Elmar Herbst; Michael T. Hirschmann; Sebastian Kopf; Romain Seil; Thomas Tischer; Robert Feldt; Kristian Samuelsson; Felix C. Oettl

doi:10.1002/jeo2.70863

A practical guide to the implementation of AI in orthopaedic research-Part 4: Prerequisites for a successful orthopedics AI-driven project in terms of interdisciplinary collaboration, data management, ethical approval and technology
Reviewartikel, 2026

Translating artificial intelligence (AI) research in orthopedics from proof-of-concept studies into production-grade clinical systems requires the systematic satisfaction of four prerequisite domains: interdisciplinary team architecture, technical data management, ethical and regulatory governance and production-grade technology and deployment infrastructure. Despite a tenfold increase in orthopedic AI publications, fewer than 6% of studies reach routine clinical deployment, reflecting persistent gaps in each of these domains. This article provides a technically rigorous, evidence-based framework organized around these four pillars. The interdisciplinary team may be structured using a product-centric topology that decouples stream-aligned clinical teams from platform infrastructure teams, following Huffman et al.'s six-step AI project lifecycle: obtain/curate/label data; establish a reference standard; develop the model; evaluate performance; externally validate and iteratively reinforce until clinical implementation is viable. Data management requires data extraction protocols, integration for bulk exports and a multi-component de-identification pipeline. A multi-stage Institutional Review Board framework governs ethical oversight, scaling from Exempt review for retrospective de-identified studies to Full Board Review with prospective validation and mandatory human-override mechanisms for interventional deployment. Responsible clinical deployment requires a multi-layer Clinical Machine Learning Operations framework, implementing privacy-preserving deployment, clinical observability, compliance audit trails and human-in-the-loop governance. Model drift has to be monitored with a degradation threshold triggering mandatory human review. Level of Evidence: Level V.

artificial intelligence

structure

management

collaboration