Enabling the AI Revolution in Healthcare
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866912265507176448 |
|---|---|
| author | Singh, Mona Siek, Katie Danks, David Ghani, Rayid Grin, Haley LaMacchia, Brian Lopresti, Daniel Toscos, Tammy |
| author_facet | Singh, Mona Siek, Katie Danks, David Ghani, Rayid Grin, Haley LaMacchia, Brian Lopresti, Daniel Toscos, Tammy |
| contents | The transformative potential of AI in healthcare - including better diagnostics, treatments, and expanded access - is currently limited by siloed patient data across multiple systems. Federal initiatives are necessary to provide critical infrastructure for health data repositories for data sharing, along with mechanisms to enable access to this data for appropriately trained computing researchers. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_05801 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Enabling the AI Revolution in Healthcare Singh, Mona Siek, Katie Danks, David Ghani, Rayid Grin, Haley LaMacchia, Brian Lopresti, Daniel Toscos, Tammy Computers and Society The transformative potential of AI in healthcare - including better diagnostics, treatments, and expanded access - is currently limited by siloed patient data across multiple systems. Federal initiatives are necessary to provide critical infrastructure for health data repositories for data sharing, along with mechanisms to enable access to this data for appropriately trained computing researchers. |
| title | Enabling the AI Revolution in Healthcare |
| topic | Computers and Society |
| url | https://arxiv.org/abs/2503.05801 |