There's a significant amount of ongoing research and pilot projects in AI for healthcare, but many technologies have not yet been widely rolled out due to issues of interpretability and fairness that need to be addressed first.

AI tools have been FDA-cleared and are already augmenting radiologists in tasks like diabetic retinopathy diagnosis.

AI has the potential to revolutionize healthcare access globally, but it is important to approach changes cautiously and not expect overnight transformations.

There is significant progress in AI applications in healthcare, particularly in image recognition tasks, which are becoming increasingly effective and reliable.

AI needs healthcare as a domain where its application can justify the high costs associated with developing AI technologies.

The healthcare ecosystem's response to AI is unpredictable due to regulations, but unregulated jobs that involve simple text processing are at high risk of automation.

AI has the potential to improve diagnosis and treatment in healthcare, moving beyond just drug discovery.

Regulating AI development is crucial, but stopping it entirely is not a viable solution, as the benefits to humanity, especially in healthcare, are significant.

The velocity and centralization of data and algorithms in AI pose challenges that require better regulatory answers.