Observational evaluation of AI-assisted measurements and reporting for enhanced workflow efficiency in leg and foot radiographs.

Marie-Pauline Talabard¹, Marion Durteste², Amine Hamid³, Gaspard Vingtrinier², Remi Quilliet², Antoine Feydy⁴, Ali Guermazi⁵, Jeanne Ventre², Louis Lassalle⁶, Nor-Eddine Regnard⁷

¹Department of Musculoskeletal Imaging, Cochin Hospital, AP-HP, Paris, France. Electronic address: marie.talabard@aphp.fr.

²Gleamer, Paris, France.

³Réseau d'Imagerie Sud Francilien, Lieusaint, France.

⁴Department of Musculoskeletal Imaging, Cochin Hospital, AP-HP, Paris, France.

⁵Department of Radiology, VA Boston Healthcare System, Boston University School of Medicine, Boston, MA, USA.

⁶Gleamer, Paris, France; Réseau d'Imagerie Sud Francilien, Lieusaint, France; Ramsay Santé, Clinique du Mousseau, Evry, France.

⁷Gleamer, Paris, France; Réseau d'Imagerie Sud Francilien, Lieusaint, France.

PubMed DOI

Abstract

RATIONALE AND OBJECTIVES: Radiographic musculoskeletal (MSK) measurements are essential for diagnosis and surgical planning, but they remain time-consuming and prone to variability. Artificial intelligence (AI) can address these limitations by automating both measurements and reporting. This study assessed the impact of AI-assisted measurements and reporting on workflow efficiency for leg and foot…

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