Demonstrating the accuracy and efficiency of artificial intelligence integrated workflows for primary breast cancer diagnosis: A comparative paired reader analysis.
- DOI
- 10.1016/j.jpi.2026.100748
- Published
- 2026 Nov
- Container
- Journal of pathology informatics
- Publisher
- Not recorded
- Open access
- yes
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Cite this work
BibTeX
@article{allodium:10.1016/j.jpi.2026.100748,
title = {Demonstrating the accuracy and efficiency of artificial intelligence integrated workflows for primary breast cancer diagnosis: A comparative paired reader analysis.},
author = {Parry J and Jindal S and Blackmur T and Channathodiyil P and Raiber-Moreau EA and Lee J and Mevorach D and Mallel G and Broderick R and Ambrogio S and Vecsler M},
year = {2026},
journal = {Journal of pathology informatics},
doi = {10.1016/j.jpi.2026.100748},
url = {https://doi.org/10.1016/j.jpi.2026.100748}
}RIS
TY - JOUR TI - Demonstrating the accuracy and efficiency of artificial intelligence integrated workflows for primary breast cancer diagnosis: A comparative paired reader analysis. AU - Parry J AU - Jindal S AU - Blackmur T AU - Channathodiyil P AU - Raiber-Moreau EA AU - Lee J AU - Mevorach D AU - Mallel G AU - Broderick R AU - Ambrogio S AU - Vecsler M PY - 2026 JO - Journal of pathology informatics DO - 10.1016/j.jpi.2026.100748 UR - https://doi.org/10.1016/j.jpi.2026.100748 ER -
APA
J, P., S, J., T, B., P, C., EA, R., J, L., D, M., G, M., R, B., S, A., & M, V. (2026). Demonstrating the accuracy and efficiency of artificial intelligence integrated workflows for primary breast cancer diagnosis: A comparative paired reader analysis.. Journal of pathology informatics. https://doi.org/10.1016/j.jpi.2026.100748
Source records
- pubmed · retrieved 2026-11-10T08:56:31.721Z