Groundhog Day for Medical Artificial Intelligence
Following a boom in investment and overinflated expectations in the 1980s, artificial intelligence entered a period of retrenchment known as the “AI winter.” With advances in the field of machine learning and the availability of large datasets for training various types of artificial neural networks...
| Main Author: | |
|---|---|
| Format: | Electronic Article |
| Language: | English |
| Check availability: | HBZ Gateway |
| Interlibrary Loan: | Interlibrary Loan for the Fachinformationsdienste (Specialized Information Services in Germany) |
| Published: |
2018
|
| In: |
The Hastings Center report
Year: 2018, Volume: 48, Issue: 3 |
| Online Access: |
Volltext (kostenfrei) Volltext (kostenfrei) |
| Summary: | Following a boom in investment and overinflated expectations in the 1980s, artificial intelligence entered a period of retrenchment known as the “AI winter.” With advances in the field of machine learning and the availability of large datasets for training various types of artificial neural networks, AI is in another cycle of halcyon days. Although medicine is particularly recalcitrant to change, applications of AI in health care have professionals in fields like radiology worried about the future of their careers and have the public tittering about the prospect of soulless machines making life-and-death decisions. Medicine thus appears to be at an inflection point—a kind of Groundhog Day on which either AI will bring a springtime of improved diagnostic and predictive practices or the shadow of public and professional fear will lead to six more metaphorical weeks of winter in medical AI. |
|---|---|
| ISSN: | 1552-146X |
| Contains: | Enthalten in: Hastings Center, The Hastings Center report
|
| Persistent identifiers: | DOI: 10.1002/hast.842 |
