Computer Science > Computers and Society
arXiv:2503.05801 (cs)
[Submitted on 3 Mar 2025]
Title:Enabling the AI Revolution in Healthcare
Authors:Mona Singh (Princeton University),Katie Siek (Indiana University Bloomington),David Danks (University of California San Diego),Rayid Ghani (Carnegie Mellon University),Haley Grin (CRA),Brian LaMacchia (MPC Alliance),Daniel Lopresti (Lehigh University),Tammy Toscos (Parkview Health)
View a PDF of the paper titled Enabling the AI Revolution in Healthcare, by Mona Singh (Princeton University) and 7 other authors
View PDFAbstract: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.
| Subjects: | Computers and Society (cs.CY) |
| Cite as: | arXiv:2503.05801 [cs.CY] |
| (orarXiv:2503.05801v1 [cs.CY] for this version) | |
| https://doi.org/10.48550/arXiv.2503.05801 arXiv-issued DOI via DataCite |
Submission history
From: Catherine Gill [view email] [via Catherine Gill as proxy][v1] Mon, 3 Mar 2025 18:17:22 UTC (498 KB)
Full-text links:
Access Paper:
- View PDF
- Other Formats
View a PDF of the paper titled Enabling the AI Revolution in Healthcare, by Mona Singh (Princeton University) and 7 other authors
References & Citations
Bookmark
Bibliographic and Citation Tools
Bibliographic Explorer(What is the Explorer?)
Connected Papers(What is Connected Papers?)
Litmaps(What is Litmaps?)
scite Smart Citations(What are Smart Citations?)
Code, Data and Media Associated with this Article
alphaXiv(What is alphaXiv?)
CatalyzeX Code Finder for Papers(What is CatalyzeX?)
DagsHub(What is DagsHub?)
Gotit.pub(What is GotitPub?)
Hugging Face(What is Huggingface?)
Papers with Code(What is Papers with Code?)
ScienceCast(What is ScienceCast?)
Demos
Recommenders and Search Tools
Influence Flower(What are Influence Flowers?)
CORE Recommender(What is CORE?)
arXivLabs: experimental projects with community collaborators
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community?Learn more about arXivLabs.