The age of foundation models
The development of clinically relevant artificial intelligence (AI) models has traditionally required access to extensive labelled datasets, which inevitably centre AI advances around large centres and private corporations. Data availability has also dictated the development of AI applications: most...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article (Journal) |
| Language: | English |
| Published: |
05 September 2024
|
| In: |
Nature reviews. Clinical oncology
Year: 2024, Volume: 21, Issue: 11, Pages: 769-770 |
| ISSN: | 1759-4782 |
| DOI: | 10.1038/s41571-024-00941-8 |
| Online Access: | Verlag, kostenfrei, Volltext: https://doi.org/10.1038/s41571-024-00941-8 Verlag, kostenfrei, Volltext: https://www.nature.com/articles/s41571-024-00941-8 
|
| Author Notes: | Jana Lipkova & Jakob Nikolas Kather |
| Summary: | The development of clinically relevant artificial intelligence (AI) models has traditionally required access to extensive labelled datasets, which inevitably centre AI advances around large centres and private corporations. Data availability has also dictated the development of AI applications: most studies focus on common cancer types, and leave rare diseases behind. However, this paradigm is changing with the advent of foundation models, which enable the training of more powerful and robust AI systems using much smaller datasets. |
|---|---|
| Item Description: | Gesehen am 28.02.2025 |
| Physical Description: | Online Resource |
| ISSN: | 1759-4782 |
| DOI: | 10.1038/s41571-024-00941-8 |