On Deep Learning, Healthcare and Qure.ai

At Qure.ai, our mission is to make healthcare more affordable and accessible using the power of deep learning. Deep learning is transforming how machines learn - from having to hand-craft features to machines automatically learning features from labeled data. Recently, deep learning has been able to perform well on tasks which require human expertise gained over years of training. Some of these include driving cars, playing games such as Go, diagnosing medical images, writing poetry or drawing paintings. The advantage machines have here is that they can go through 10,000 CT Scan images or play 100,000 games in a matter of a few hours, a number which might take a human several years or even a whole lifetime.

On the other hand, healthcare is going through a revolution of its own. The amount of medical data generated is growing exponentially, from medical images to connected devices. The use of diagnostic imaging has increased significantly and continues to grow. CT scans grow at 7.8 percent annually and MRIs at 10% every year. This has an impact on the workload of healthcare practitioners - in 1999 radiologists were expected to interpret 2.8 images per minute which had increased to 19 images per minute in 2010. Additionally, new kinds of healthcare data (such as that generated by genome sequencing and biosensors) are large and complex, rendering traditional diagnostic methods obsolete. These sources of data also continue to grow – 200,000 genomes had been sequenced by 2014 and the number is expected to reach 1.7 million by 2017. Unfortunately, trained physicians who would analyze this data are growing at a much slower rate. For example, the growth of radiologists is only half of the growth in medical images. As organized healthcare reaches parts of the developing world who had no healthcare access previously, this ratio suffers further.

We believe that Artificial Intelligence (AI) will be critical in ensuring that healthcare practitioners can focus on cases that truly matter, letting machines diagnose or treat the easier ones. AI will also contribute in combining various data sources from a patient’s history (medical records, radiology imaging, pathology imaging, genome sequences, fitness band data, heart monitor data, etc.) to generate a personalized diagnosis or a personalized treatment plan. At Qure.ai, we use deep learning to diagnose disease from radiology and pathology imaging, and to create personalized cancer treatment plans from histopathology imaging and genome sequences.

We are a team of computer scientists, medical practitioners and bioinformaticians who believe that our work can have an enormous impact on human lives. We are hiring! If deep learning is an area that excites you, come join us!

Our research is done in collaboration with several hospital chains, universities and research institutions. If you are a medical institution and would like to collaborate with us, please reach out to us.