Trustworthy artificial intelligence in radiation oncology: cross-industry lessons for development, validation, and deployment

Malinda Zhu, Lang Gou, Chi Zhang, Dandan Zheng

Open source

DOI: 10.3389/fonc.2026.1912362
Published: 2026-07-23
Container: Frontiers in Oncology
Publisher: Frontiers Media SA
Open access: unknown

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Cite this work

BibTeX

@article{allodium:10.3389/fonc.2026.1912362,
  title = {Trustworthy artificial intelligence in radiation oncology: cross-industry lessons for development, validation, and deployment},
  author = {Malinda Zhu and Lang Gou and Chi Zhang and Dandan Zheng},
  year = {2026},
  journal = {Frontiers in Oncology},
  doi = {10.3389/fonc.2026.1912362},
  url = {https://doi.org/10.3389/fonc.2026.1912362}
}

RIS

TY  - JOUR
TI  - Trustworthy artificial intelligence in radiation oncology: cross-industry lessons for development, validation, and deployment
AU  - Malinda Zhu
AU  - Lang Gou
AU  - Chi Zhang
AU  - Dandan Zheng
PY  - 2026
JO  - Frontiers in Oncology
DO  - 10.3389/fonc.2026.1912362
UR  - https://doi.org/10.3389/fonc.2026.1912362
ER  -

APA

Zhu, M., Gou, L., Zhang, C., & Zheng, D. (2026). Trustworthy artificial intelligence in radiation oncology: cross-industry lessons for development, validation, and deployment. Frontiers in Oncology. https://doi.org/10.3389/fonc.2026.1912362

Source records

crossref · retrieved 2026-09-26T13:11:19.732Z