| dc.contributor.author | Methot, John | |
| dc.contributor.author | Antell, Gregory | |
| dc.contributor.author | Umeton, Renato | |
| dc.date.accessioned | 2025-05-07T18:46:10Z | |
| dc.date.available | 2025-05-07T18:46:10Z | |
| dc.date.issued | 2020-11-14 | |
| dc.identifier.uri | https://hdl.handle.net/1721.1/159236 | |
| dc.description | Proceedings of AMIA 2020, American Medical Informatics Association Annual Symposium, USA, November 14-18, 2020 | en_US |
| dc.description.abstract | Few AI applications in oncology have progressed to production or clinical use. This translational hurdle has two
main components: static or limited training data; and the absence of a production environment into which models
may be deployed. Dana-Farber's Platform for Operationalized Data Science aims to remove these impediments to
enable development and deployment of AI in a healthcare setting at scale. | en_US |
| dc.publisher | American Medical Informatics Association | en_US |
| dc.rights | Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use. | en_US |
| dc.title | Operationalizing Artificial Intelligence: Lessons Learned at Dana-Farber Cancer Institute | en_US |
| dc.type | Article | en_US |
| dc.identifier.citation | Methot, John, Antell, Gregory and Umeton, Renato. 2020. "Operationalizing Artificial Intelligence: Lessons Learned at Dana-Farber Cancer Institute." | |
| dc.eprint.version | Final published version | en_US |
| dc.type.uri | http://purl.org/eprint/type/ConferencePaper | en_US |
| eprint.status | http://purl.org/eprint/status/NonPeerReviewed | en_US |
| dspace.date.submission | 2025-04-18T14:52:25Z | |
| mit.license | PUBLISHER_POLICY | |
| mit.metadata.status | Authority Work and Publication Information Needed | en_US |
