A new study from Johns Hopkins University suggests that general-purpose AI models, trained on varied data, can match or exceed the performance of specialized models in healthcare tasks, challenging long-held views on the necessity for specialization.
The results indicate that general models performed as well or better than specialized medical models in about 88% of medical tasks, suggesting that the previous focus on designing specialized models may be excessive.
Researchers found that a well-crafted prompt could maximize the effectiveness of large language models, effectively bridging the gap between general and specialized AI capabilities.
While general models perform well overall, the study emphasizes that specialization still plays a critical role in high-risk medical tasks, where tailored models may be necessary.
Collection
[
|
...
]