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

We partner with the largest satellite companies to source high-quality imagery and collect lidar data, which is crucial for improving our algorithms and expanding our model globally.

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

Our approach to AI in medicine involves using smaller, specialized models trained on peer-reviewed literature that outperform larger models on specific medical tasks, which was a novel insight at the time of development.

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.

AI can analyze medical images, but establishing a reliable ground truth for diagnoses can be challenging due to differing opinions among experts.

In my postdoc, I shifted towards inverse imaging problems, where the observed data is not a direct image but a transform, such as in CT scans.

To improve accuracy, we labeled 150,000 ophthalmology images with input from seven ophthalmologists, reducing variance in scoring.

The challenge in CT imaging is that the data collected is often insufficient for high-resolution images, leading to noise in the measurements.