Abstract
Deep medicine, which aims to push the boundaries of artificial intelligence to reshape the health and medical intelligence and decision making, is a promising concept that is gaining attention over traditional EMR-based medical information management systems. The success of intelligent solutions in health and medicine depends on the degree to which they support interoperability, to allow consistent integration of different systems and data sources, and explainability, to make their decisions understandable, interpretable, and justifiable by humans.
Original language | English (US) |
---|---|
Title of host publication | Explainable AI in Healthcare and Medicine - Building a Culture of Transparency and Accountability |
Editors | Arash Shaban-Nejad, Martin Michalowski, David L. Buckeridge |
Publisher | Springer Science and Business Media Deutschland GmbH |
Pages | 1-10 |
Number of pages | 10 |
ISBN (Print) | 9783030533519 |
DOIs | |
State | Published - Nov 3 2020 |
Event | AAAI International Workshop on Health Intelligence, W3PHIAI 2020 - New York City, United States Duration: Feb 7 2020 → Feb 7 2020 |
Publication series
Name | Studies in Computational Intelligence |
---|---|
Volume | 914 |
ISSN (Print) | 1860-949X |
ISSN (Electronic) | 1860-9503 |
Conference
Conference | AAAI International Workshop on Health Intelligence, W3PHIAI 2020 |
---|---|
Country/Territory | United States |
City | New York City |
Period | 2/7/20 → 2/7/20 |
Bibliographical note
Publisher Copyright:© 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.
Keywords
- Deep medicine
- Explainable AI
- Interpretability
- Precision medicine