Innovative technologies, like AI for diabetic retinopathy screening, face regulatory hurdles due to the lack of appropriate coding, which delays their implementation in healthcare.

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

AI will significantly improve healthcare by reading medical images better than human experts and providing more effective family doctor services by integrating vast amounts of patient data.

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

We expect to see meaningful progress against diseases with AI-assisted tools in the near future.

Our model for detecting diabetic retinopathy now performs comparably to the median of eight US board-certified ophthalmologists, which is significant given the shortage of specialists in many regions.

Using AI in high-stakes situations, like tumor board meetings, can significantly improve decision-making processes.

In the medical domain, we applied deep learning to ophthalmology, specifically to detect diabetic retinopathy in retinal images.

AI tools are augmenting human capabilities, and while some jobs may be disrupted, new and more valuable roles will emerge, requiring a shift in the skills that are prioritized in education.