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

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

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

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

In medical diagnostics, models can demonstrate impressive reasoning capabilities, mapping symptoms to correct medical cases effectively.

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 can help create a new gold standard in medical diagnosis by providing consistent results where human experts may disagree.

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.