MedVidDeID: Protecting privacy in clinical encounter video recordings
The MedVidDeID pipeline offers a viable, efficient hybrid solution for handling AV healthcare data and privacy preservation. Future work will focus on reducing upstream errors at each stage and minimizing the role of the human in the loop.
Authors
- Sydney Pugh (ORCID: https://orcid.org/0000-0002-1465-983X)
- Sy Hwang (ORCID: https://orcid.org/0000-0003-3851-9521)
- Eric R. Eaton (ORCID: https://orcid.org/0000-0002-5689-2234)
- Ashley Batugo
- Kevin B. Johnson (ORCID: https://orcid.org/0000-0003-1016-9363)
- Kuk Jin Jang (ORCID: https://orcid.org/0000-0002-3134-3220)
- Jean Park (ORCID: https://orcid.org/0000-0002-7394-7452)
- Danielle L. Mowery (ORCID: https://orcid.org/0000-0003-3802-4457)
- Basam Alasaly (ORCID: https://orcid.org/0000-0002-0805-595X)
- Sriharsha Mopidevi (ORCID: https://orcid.org/0009-0002-6065-0251)
Institutions
- University of Pennsylvania (US)
- Philadelphia University (US)
- Hongik University (KR)
Publication Details
- Journal
- Journal of Biomedical Informatics
- Published
- 2025-08-29
- DOI
- https://doi.org/10.1016/j.jbi.2025.104901
- Primary Topic
- Artificial Intelligence in Healthcare and Education
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Institutes of Health