Revolutionising healthcare by empowering doctors with artificial intelligence
We want to put an accessible and affordable health service into the hands of every person on Earth. And to do that, we’re combining human expertise with the power of technology. We built a suite of tools, designed around a doctor’s brain, using Artificial Intelligence (AI). It’s what makes us so different from other healthcare providers.
AI is a transformational force in healthcare. It helps medical professionals work faster, see more patients, and make decisions based on user-inputted information. It helps patients address symptoms, get faster information about conditions, and proceed to treatment sooner.
Our AI system can efficiently read, comprehend, and learn from anonymized, aggregated, and medical datasets—when patients give consent for us to use their health information in this way. And our complementary set of AI tools can help make decisions about triage, causes of symptoms, and future health predictions. Our work has led to more than 20 peer-reviewed papers and 30 groups of patents.
The beginner’s guide to AI in healthcare
We get it. All this talk about AI can be a bit complicated and confusing. That’s why we’ve created a handy guide to help you understand what we mean when we talk about using AI to revolutionize the health system.
If you understand the ins and outs of AI, feel free to skip the guide and scroll down for more details.
Learn more about our AI
With advances in mathematics, computational power and the availability of data, the field of AI has progressed rapidly in recent years. We’re surrounded by articles and opinions about it, which has led to confusion about what it is - and isn’t.
We’ve made great progress so far
1. We’re proving the credibility of AI in primary care.
2. We’ve built an AI for medicine that is not just a ‘black box’.
3. We’re deploying AI in healthcare at scale
4. We’ve pushed what natural language processing can do.
We’re part of the community
We contribute to the AI community by publishing papers, speaking at conferences and open-sourcing some of our work for the benefit of all.
Estimating Mutual Information Between Dense Word Embeddings
Vitalii Zhelezniak, Aleksandar Savkov, April Shen, Nils Hammerla
Hybrid Reasoning Over Large Knowledge Bases Using On-The-Fly Knowledge Extraction
Stoilos, Giorgos and Juric, Damir and Wartak, Szymon and Schulz, Claudia and Khodadadi, Mohammad
Can Embeddings Adequately Represent Medical Terminology? New Large-Scale Medical Term Similarity Datasets
Claudia Schulz, Damir Juric