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

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

The AI community is actively addressing issues of fairness, accountability, and transparency, which are crucial for the responsible deployment of AI technologies.

Accuracy and transparency are crucial in AI healthcare applications, allowing physicians to interrogate the sources of information provided by the AI.

The reliability of AI systems is a major concern as we transition from consumer applications to mission-critical systems like healthcare and defense.

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

The workshop 'AI in the Wild' highlights the shift from lab-based AI research to addressing practical deployment challenges in real-world scenarios, including the deployment of AI in healthcare, which is still largely in pilot phases, with specific applications like skin lesion analysis being among the few widely used examples.

AI technology is rapidly improving, with current systems being less biased and more fair than they were six months ago, although challenges with bias and fairness still exist.

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