Is Artificial Intelligence Better Than Human Clinicians in Predicting Patient Outcomes?
In contrast with medical imaging diagnostics powered by artificial intelligence (AI), in which deep learning has led to breakthroughs in recent years, patient outcome prediction poses an inherently challenging problem because it focuses on events that have not yet occurred. Interestingly,...
| Main Author: | |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
JMIR Publications
2020-08-01
|
| Series: | Journal of Medical Internet Research |
| Online Access: | http://www.jmir.org/2020/8/e19918/ |
| Summary: | In contrast with medical imaging diagnostics powered by artificial intelligence (AI), in which deep learning has led to breakthroughs in recent years, patient outcome prediction poses an inherently challenging problem because it focuses on events that have not yet occurred. Interestingly, the performance of machine learning–based patient outcome prediction models has rarely been compared with that of human clinicians in the literature. Human intuition and insight may be sources of underused predictive information that AI will not be able to identify in electronic data. Both human and AI predictions should be investigated together with the aim of achieving a human-AI symbiosis that synergistically and complementarily combines AI with the predictive abilities of clinicians. |
|---|---|
| ISSN: | 1438-8871 |