Learn more about our AI
Our AI, developed by our team of research scientists, engineers and healthcare professionals, is a suite of AI tools designed around a doctor’s brain to provide accessible healthcare for millions. A number of these tools are modular, so they can be standalone and used in isolation, or combined to suit different requirements. We’re working with partners, from governments and foundations, to businesses and pharmaceuticals, to tech companies and telcos, tailoring our platform to meet their specific needs.
What it does
We’ve designed our AI to empower people with knowledge of their health, with the aim of relieving pressure on clinicians. It mimics the way a doctor operates, performing some of the cognitive tasks they carry out.
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Perceive
Our AI uses aggregated, anonymised and consented data from various sources such as medical datasets - but it can also read and learn from patient health records, including the consultation notes made by our clinicians in the different countries where we work. This is a huge feat as clinicians use many different terms, and can have very different writing styles, even when describing the same medical issues.
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Reason and make decisions
It can reason in order to decide on and provide information about the likely causes for people’s symptoms. It then recommends next steps, including treatment information. It can also assess your disease risk based on your current health and behaviour.
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Simulate
It can make predictions based on facts and observations. It simulates future potential outcomes to coach and nudge behavioural change to reduce the likelihood of future illness.
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Learn
It learns with every experience, meaning it gets more accurate and can make interactions more personalised over time. Each component of our AI reinforces the next, creating a continuous learning loop.
How it works
Our AI revolves around four main parts - the knowledge base, the comprehensive health record, the probabilistic graphical model, and simulations.
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Knowledge base
Central to our AI is a form of digital encyclopaedia of medicine (our knowledge base) that contains the definitions, characteristics and relationships of the different diseases, symptoms and treatments. It contextualises this information with a graphical representation that shows the relationships between the medical components.
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The comprehensive health record
This holds all of the available information about our individual users, including their medical history and consented data gathered through interacting with Babylon. It helps us make connections between users and different types of conditions, and their likely progressions over time.
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Probabilistic graphical model
This uses the knowledge from our digital encyclopaedia, combined with all the data to test different models about illnesses. It is capable of processing millions of combinations of symptoms, diseases and risk factors per second, to help identify conditions which may match the information entered. A similar approach is also used to predict disease risks over the next five years.
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Simulations
Simulations are used to estimate ‘what-if’ scenarios, to predict what happens if people continue their routines for diet, exercise, sleep and stress. It helps users understand the impact of their actions and helps us develop optimised care plans for them.
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.
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Estimating Mutual Information Between Dense Word Embeddings
Vitalii Zhelezniak, Aleksandar Savkov, April Shen, Nils Hammerla
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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
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Can Embeddings Adequately Represent Medical Terminology? New Large-Scale Medical Term Similarity Datasets
Claudia Schulz, Damir Juric