Synthesized clinical notes enable training robust multimodal AI models from unimodal dermatology datasets.

Marini N, Liang Z, Rajaraman S, Xue Z, Antani S

DOI: 10.1038/s41746-026-03040-3
Published: 2026 Jul 17
Container: NPJ digital medicine
Publisher: Not recorded
Open access: unknown

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Cite this work

BibTeX

@article{allodium:10.1038/s41746-026-03040-3,
  title = {Synthesized clinical notes enable training robust multimodal AI models from unimodal dermatology datasets.},
  author = {Marini N and Liang Z and Rajaraman S and Xue Z and Antani S},
  year = {2026},
  journal = {NPJ digital medicine},
  doi = {10.1038/s41746-026-03040-3},
  url = {https://doi.org/10.1038/s41746-026-03040-3}
}

RIS

TY  - JOUR
TI  - Synthesized clinical notes enable training robust multimodal AI models from unimodal dermatology datasets.
AU  - Marini N
AU  - Liang Z
AU  - Rajaraman S
AU  - Xue Z
AU  - Antani S
PY  - 2026
JO  - NPJ digital medicine
DO  - 10.1038/s41746-026-03040-3
UR  - https://doi.org/10.1038/s41746-026-03040-3
ER  -

APA

N, M., Z, L., S, R., Z, X., & S, A. (2026). Synthesized clinical notes enable training robust multimodal AI models from unimodal dermatology datasets.. NPJ digital medicine. https://doi.org/10.1038/s41746-026-03040-3

Source records

pubmed · retrieved 2026-09-25T19:56:48.243Z