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Babylon Health

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 our AI does

We’ve designed our AI tools to empower people with knowledge of their health, with the goal of relieving pressure on clinicians.


Our AI can efficiently read, comprehend, and learn from anonymized, aggregated, and medical datasets—when patients give consent for us to use their health information.

AI icon of gears in a head representing Babylon's triage system for analyzing symptoms and suggesting treatment.


It can use data in order to decide on, and provide information about, the likely causes for people’s symptoms. It then suggests possible next steps, including treatment information. It can let you know about general risks for certain conditions, when comparing user-inputted information and generally available data.

How it works

Our AI revolves around four main parts - the knowledge base, the comprehensive health record, the probabilistic graphical model.

Knowledge base

Central to our AI is a form of digital encyclopedia of medicine (our knowledge base) that contains the definitions, characteristics and relationships of certain diseases, symptoms and treatments. It contextualizes this information with a graphical representation that shows the relationships between the medical components.

The comprehensive health record

This holds all of the available information about our individual users, when they give consent for us to use their health information. The record includes 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 when compared against generally available data.

Probabilistic graphical model

This uses the knowledge from our digital encyclopedia, combined with all the data to test different models about certain illnesses. It helps identify conditions which may match the information entered. A similar approach is also used to predict disease risks over the next five years when compared against generally available data.


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 optimized 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.

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