Step by step, condition by condition, AI systems are slowly learning to diagnose disease as well as any human doctor, and they could soon be working in a hospital near you. The latest example is from London, where researchers from Google’s DeepMind subsidiary, UCL, and Moorfields Eye Hospital have used deep learning to create software that identifies dozens of common eye diseases from 3D scans and then recommends the patient for treatment.
The work is the result of a multiyear collaboration between the three institutions. And while the software is not ready for clinical use, it could be deployed in hospitals in a matter of years. Those involved in the research described is as “ground-breaking.” Mustafa Suleyman, head of DeepMind Health, said in a press statement that the project was “incredibly exciting” and could, in time, “transform the diagnosis, treatment, and management of patients with sight threatening eye conditions […] around the world.”
The software, described in a paper published in the journal Nature Medicine, is based on established principles of deep learning, which uses algorithms to identify common patterns in data. In this case, the data is 3D scans of patients’ eyes made using a technique known as optical coherence tomography, or OCT. Creating these scans takes around 10 minutes and involves bouncing near-infrared light off of the interior surfaces of the eye. Doing so creates a 3D image of the tissue, which is a common way to assess eye health. OCT scans are a crucial medical tool, as early identification of eye disease often saves the patient’s sight.
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