Artificial intelligence for automatic FLS credentialing: highlighting and addressing current limitations.

Sestini L, Harris M, Alapatt D, Yu T, Mascagni P, Swanström LL, Padoy N, Shlomovitz E.

DOI: 10.1007/s00464-025-12076-7
Published: 2025-08-27
Container: Surg Endosc
Publisher: Not recorded
Open access: no

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Cite this work

BibTeX

@article{allodium:10.1007/s00464-025-12076-7,
  title = {Artificial intelligence for automatic FLS credentialing: highlighting and addressing current limitations.},
  author = {Sestini L and  Harris M and  Alapatt D and  Yu T and  Mascagni P and  Swanström LL and  Padoy N and  Shlomovitz E.},
  year = {2025},
  journal = {Surg Endosc},
  doi = {10.1007/s00464-025-12076-7},
  url = {https://doi.org/10.1007/s00464-025-12076-7}
}

RIS

TY  - JOUR
TI  - Artificial intelligence for automatic FLS credentialing: highlighting and addressing current limitations.
AU  - Sestini L
AU  -  Harris M
AU  -  Alapatt D
AU  -  Yu T
AU  -  Mascagni P
AU  -  Swanström LL
AU  -  Padoy N
AU  -  Shlomovitz E.
PY  - 2025
JO  - Surg Endosc
DO  - 10.1007/s00464-025-12076-7
UR  - https://doi.org/10.1007/s00464-025-12076-7
ER  -

APA

L, S., M, H., D, A., T, Y., P, M., LL, S., N, P., & E., S. (2025). Artificial intelligence for automatic FLS credentialing: highlighting and addressing current limitations.. Surg Endosc. https://doi.org/10.1007/s00464-025-12076-7

Source records

europe-pmc · retrieved 2026-09-25T07:52:15.948Z