In partnership with healthcare organizations globally, we’re researching robust new AI-enabled tools focused on diagnostics to assist clinicians. Drawing from diverse datasets, high-quality labels, and state-of-the-art deep learning techniques, we are making models that we hope will eventually support medical specialists in diagnosing disease. We’re excited to further develop this research towards new frontiers—and to demonstrate that AI has the ability to enable novel, transformative diagnostics.
This product has been CE marked as a Class I medical device in the EU. It is not available in the United States. This research is not related to the DermAssist tool, which is no longer in development.
Through computer vision AI and image search capabilities, we are developing a tool to help individuals better research & identify their skin, hair, and nail conditions. The tool supports hundreds of conditions, including more than 80% of the conditions seen in clinics and more than 90% of the most commonly searched conditions. The work was highlighted in both Nature Medicine and JAMA Network Open.
Automated Retinal Disease Assessment (ARDA) is being used to help clinicians detect diabetic retinopathy, a leading cause of blindness, in India and across the world. With widespread adoption, perhaps millions of patients with diabetes could keep their vision in part to ARDA assisting doctors. Our research was published in JAMA and Ophthalmology. Additional research, published in Lancet Digital Health, showed that we can predict whether patients will develop diabetic retinopathy in the future, which can help doctors customize both treatment and eye screening frequencies for their patients. The solution is currently being evaluated in clinical studies in the United States as well as in Thailand. Learn more.
Lung cancer leads to over 1.8 million deaths per year world wide, accounting for almost one in five cancer deaths, and is the largest cause of cancer mortality. Our research, published in Nature Medicine, shows that deep learning may eventually help physicians more accurately screen for lung cancer and identify the disease even in incidental lung cancer detection workflows. Read the post
The human eye can reveal signs of underlying diseases like anemia, a condition that affects 1.6 billion people worldwide causing tiredness, weakness, dizziness and drowsiness. In research published in Nature Biomedical Engineering, we were able to use deep learning to quantify hemoglobin levels and detect anemia with de-identified photographs of the back of the eye. This result means it’s possible that someday providers may be able to detect the disease with a simple non-invasive screening tool. Read the post
Assessing the risk of cardiovascular diseases is the first and most critical step toward reducing the likelihood that a patient suffers a cardiovascular event in the future. By applying deep learning techniques to retinal images, we’ve been able to reveal factors associated with the risk of a major cardiovascular event like a heart attack or stroke, as published in Nature Biomedical Engineering. This research could help scientists generate more targeted hypotheses and drive a wide range of future research. Read the post
Breast cancer screening helps detect cancer earlier, but diagnosing breast cancer accurately and consistently remains a challenge, with half of all women experiencing a false-positive over a 10-year period. In Nature, we demonstrated the potential of our AI model to analyze de-identified retrospectively collected screening mammograms with similar or better accuracy than clinicians. Now, we’re collaborating on an investigative device research study to understand how the model can help in clinical practice to reduce the time from screening mammography to diagnosis, narrowing the assessment gap and improving the patient experience. Read the post
In our pathology research published in the Archives of Pathology & Laboratory Medicine as well as The American Journal of Surgical Pathology, we showed how a proof-of-concept assistance tool (LYNA) could use deep learning to increase the accuracy of metastatic breast cancer detection. Read the post
We’re doing research and building AI models that can not only decipher important information from retinal images, but rather from external eye images. In our research published in The Lancet Digital Health, we show that a deep learning model can predict the presence of diabetic retinal disease and other biomarkers such as HbA1c or eGFR from external eye images alone. This could reduce the need for specialized equipment and expand access to care for the growing population of patients with diabetes or other chronic diseases. Read the post
We continue to advance AI-enabled imaging research in other domains, expanding this technology to facilitate transformative diagnostics.