ATTEST 2027: A Framework for AI Transparency Tiered by Evidentiary Stakes

Wanzhu Tu, Jun Li, Michael Charlton, Naga Chalasani, Laura E. Nagy

Open source

DOI: 10.1097/hep.0000000000001912
Published: 2026-11-05
Container: Hepatology
Publisher: Ovid Technologies (Wolters Kluwer Health)
Open access: unknown

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Cite this work

BibTeX

@article{allodium:10.1097/hep.0000000000001912,
  title = {ATTEST 2027: A Framework for AI Transparency Tiered by Evidentiary Stakes},
  author = {Wanzhu Tu and Jun Li and Michael Charlton and Naga Chalasani and Laura E. Nagy},
  year = {2026},
  journal = {Hepatology},
  doi = {10.1097/hep.0000000000001912},
  url = {https://doi.org/10.1097/hep.0000000000001912}
}

RIS

TY  - JOUR
TI  - ATTEST 2027: A Framework for AI Transparency Tiered by Evidentiary Stakes
AU  - Wanzhu Tu
AU  - Jun Li
AU  - Michael Charlton
AU  - Naga Chalasani
AU  - Laura E. Nagy
PY  - 2026
JO  - Hepatology
DO  - 10.1097/hep.0000000000001912
UR  - https://doi.org/10.1097/hep.0000000000001912
ER  -

APA

Tu, W., Li, J., Charlton, M., Chalasani, N., & Nagy, L. E. (2026). ATTEST 2027: A Framework for AI Transparency Tiered by Evidentiary Stakes. Hepatology. https://doi.org/10.1097/hep.0000000000001912

Source records

crossref · retrieved 2026-11-09T04:54:37.401Z