Healthcare and biosciences

We think that AI is poised to transform medicine, delivering new, assistive technologies that will empower doctors to better serve their patients.

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.

A few of our projects

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.

Diagnosing Diabetic Eye Disease

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.

Collaborating closely with doctors and international healthcare systems, we developed a state-of-the-art computer vision system for reading retinal fundus images for diabetic retinopathy and determined our algorithm’s performance is on par with U.S. board-certified ophthalmologists. We’ve recently published some of our research in the Journal of the American Medical Association and summarized the highlights in a blog post. It’s early days, and there’s still work to do to bring the benefits of this research to patients, but ultimately, we hope to help real doctors and clinics expand global screening capacity to cover all at-risk individuals in the world.

Assisting Pathologists in Detecting Cancer

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.

A closeup of a lymph node biopsy. The tissue contains a breast cancer metastasis as well as macrophages, which look similar to a tumor but are benign normal tissue. Our algorithm successfully identifies the tumor region (bright green) and is not confused by the macrophages.

To address these issues of limited time and diagnostic variability, we built an automated detection algorithm that can naturally complement pathologists’ workflow. Our algorithm was designed to be highly sensitive to make it easier for pathologists to find even small instances of breast cancer metastasis in lymph node biopsies. You can learn more about our findings in this paper and blog post.

We’re encouraged by these results, but we think they’re just the beginning. There’s significant opportunity for AI to improve the accuracy and availability of healthcare, and we hope that our research will serve as one of many demonstrations of that potential.

As part of this ongoing exploration, we’re also partnering with healthcare providers to see how machine learning might predict healthcare events. You can read more about our research collaborations in this blog post.


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.

Our first major project is DeepVariant, a universal SNP and small indel variant caller created using deep neural networks. DeepVariant is a collaboration between Google and Verily Life Sciences, and is available as open source. Read more about DeepVariant on the Google AI blog.

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10 People Involved

David Alexander, Akosua Busia, Pi-Chuan Chang, Thomas Colthurst, Mark DePristo, Lizzie Dorfman, Cory McLean, Ryan Poplin, Scott Schwartz, Suhani Vora


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