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Congress: ECR25
Poster Number: C-12171
Type: Poster: EPOS Radiologist (scientific)
DOI: 10.26044/ecr2025/C-12171
Authorblock: C. Thouly1, B. Dufour1, N. Heracleous1, D. Moreira1, D. Ribeiro1, B. Rizk1, F. Zanca2; 1Sion/CH, 2Leuven/BE
Disclosures:
Cyril Thouly: Nothing to disclose
Benoît Dufour: Nothing to disclose
Natalie Heracleous: Nothing to disclose
Daniela Moreira: Nothing to disclose
Diana Ribeiro: Nothing to disclose
Benoît Rizk: Nothing to disclose
Federica Zanca: Nothing to disclose
Keywords: Artificial Intelligence, Bones, Paediatric, Conventional radiography, Digital radiography, RIS, Computer Applications-General, Cost-effectiveness, Structured reporting, Education and training, Outcomes
Conclusion

We conducted a multi-center study using the BoneXpert AI algorithm, replacing manual estimation of bone age with an AI-driven workflow. Results show a 55% reduction in reporting time compared to the traditional Greulich and Pyle method. The findings of this study confirm that AI-assisted bone age assessment significantly enhances radiology workflow efficiency, reducing reporting time by 55% without compromising diagnostic quality. The automatic integration of AI-generated results into structured reports streamlines the reporting process, allowing radiologists to focus on more complex cases. As AI adoption continues to expand in medical imaging, this study underscores the potential for AI-driven pre-filled reports to become a new standard in diagnostic radiology, improving both accuracy and efficiency.

GALLERY