Speaker
Description
Artificial Intelligence is over-hyped and anthropomorphised by the media which has led to numerous unfulfilled expectations and fear-mongering throughout the decades. Despite experts prophesying omnipotent robot AGIs, there is seldom any discourse about the actual hindrances to adaptation of current narrow solutions. Black-box functionality, lack of transparency and interpretability, and misleading evaluation metrics of Machine Learning models are the main reasons why physicians remain skeptic towards the true potential of AI. Instead of jumping the hype, one should think of Machine Learning models as another set of sophisticated diagnostic tools that rely on patient data instead of the measurement of their bodily functions - another set of tools with their own use-cases and limitations. Instead of aiming to "replace doctors", the industry should instead create devices that help physicians establish more accurate diagnoses, in close collaboration.
