We think that AI is poised to transform medicine, delivering new, assistive technologies that will empower doctors to better serve their patients.
About the team
Machine learning has dozens of possible application areas, but healthcare stands out as a remarkable opportunity to benefit people — and working closely with clinicians and medical providers, we're developing tools that we hope will dramatically improve the availability and accuracy of medical services.
Read about some of our recent work and collaborations on the Google AI blog.
Deep learning has already revolutionized the field of computer vision, making practical, in-your-pocket technologies out of what seemed like science fiction just a few years ago. If these new computer vision systems can reach human-level accuracy in identifying dog breeds or cars, we asked ourselves, might those same systems be capable of learning to identify disease in medical images? Over the last few years, we've been working with doctors and clinicians to explore this question, and our research has shown that this is indeed possible — and not just in some far off future, but today. Two of the areas we're most excited about and where we've made the most progress in research to date are ophthalmology and digital pathology.
In the area of ophthalmology, we began exploring computer-aided diagnostic screening for a disease of the eye called diabetic retinopathy. Diabetic retinopathy is the fastest growing cause of preventable blindness globally. The condition is normally diagnosed by a highly trained doctor examining a retinal scan of the eye. If caught early, effective treatments are available, but if undetected, the disease progresses into irreversible blindness, and in much of the world, there simply are not enough doctors available to support the volume of screening required to protect the population.
In the field of digital pathology, we've focused our initial research on algorithms that might assist pathologists in detecting breast cancer in lymph node biopsies. Reviewing pathology slides is a complex task that requires years of training, expertise, and experience. Even with this extensive training, there can be substantial variability in the diagnoses given by different pathologists for the same patient — which isn't surprising given the massive amount of information that pathologists must review in order to make an accurate diagnosis.
The genomics team in Google Brain focuses on ways that deep learning can transform genome sciences, with a goal of enabling, creating and validating new capabilities and tools that will empower researchers, accelerate discoveries, and ultimately improve people's lives. Our efforts fall into three broad areas: (1) extending TensorFlow to better support genomics data; (2) developing deep learning models for genomics problems; and (3) releasing new tools and capabilities as open source software.