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Welcome to the front line of your future

We combine cutting edge AI and broader technologies with human expertise to re-engineer a better model of healthcare

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🧭 Mission and Product


2500 years ago, in the ancient city of Babylon, folks needing medical advice would gather in the town square and share thoughts on treatments for common illnesses. This is one of the earliest examples of democratizing healthcare - It’s no surprise that Babylonians enjoyed the longest life expectancy across the ancient world
Our mission is to take this simple ideology, apply radically modern technology, and put an accessible and affordable health service in the hands of every person on Earth 🌎 🙌.
To make healthcare accessible, we deliver it through the devices people already own.
To make healthcare affordable, we use technology to help ease the burden on our doctors.
By automating their routine tasks, we allow doctors to focus on what they do best: give care to the patients who need it most.
We also use our tech to step in early and help people become more informed about diseases — so they can make informed decisions and stay healthier for longer.


We operate in the healthtech sector and the mission of the company is to provide affordable and accessible healthcare services to everyone globally.
We do this through three core product offerings
Video appointment service
This allows patients to have a video appointment remotely with a doctor, at home, at work, or on the move. I think the benefits of this have been recognized even more during the COVID pandemic.
Symptom checker
This allows patients to present their symptoms to our symptom checker; this is essentially an AI chatbot. This chatbot utilizes artificial intelligence to give the patient a probable diagnosis based on the symptoms they enter.
These first two products are focused around sick-care and when a patient is actually ill.
Healthcheck service
Focuses heavily around predictive and preventative healthcare.
This allows patients to build a personal health profile for themselves. They can answer a series of health related questions, they can also input data about themselves covering things such as diet, lifestyle choices and past family medical history alongside syncing it with devices to track things such as daily steps and heart rate.
All of that data is then used and utilized using AI and machine learning to predict future illnesses or ailments that the patient is potentially more at risk from in the future, and it then works to give the patient a list of preventative measures that they can take, or lifestyle changes that they can make to reduce those risks in the future.


AI Research

Joseph Enguehard, Dan Busbridge, Adam Bozson, Claire Woodcock, Nils Hammerla
To address their temporal nature, we treat EHRs as samples generated by a Temporal Point Process (TPP), enabling us to model what happened in an event with when it happened in a principled way. Our proposed attention-based Neural TPP performs favourably compared to existing models, and provides insight into how it models the EHR, an important step towards a component of clinical decision support systems.
Published in 2020 | ML for Health Workshop, NeurIPS 2020
Adam Baker, Yura Perov, Katherine Middleton, Janie Baxter, Daniel Mullarkey, Davinder Sangar, Mobasher Butt, Arnold DoRosario
We performed a validation study of the accuracy and safety of the babylon AI triage system and human doctors using a set of identical clinical cases. Overall, we found that the AI system is able to provide patients with triage and diagnostic information with a level of clinical accuracy and safety comparable to that of human doctors.
Published in 2020 | Frontiers in Artificial Intelligence Medicine and Public Health
Yuanzhao Zhang, Robert Walecki, Joanne Winter, Felix Bragman, Sara Lourenco, Chris Hart, Adam Baker, Yura Perov and Saurabh Johri
AI-driven digital health tools often rely on estimates of disease incidence or prevalence, but obtaining these estimates is costly and time-consuming. We demonstrate that context-aware machine learning models can be used for estimating disease incidence. These methods are quicker to implement than traditional epidemiological approaches. We therefore suggest it complements existing modelling efforts, where data is required more rapidly or at larger scale. This may particularly benefit AI-driven digital health products where the data will undergo further processing and a validated approximation of the disease incidence is adequate.
Published in 2020 | Frontiers in Artificial Intelligence Medicine and Public Health
Claudia Schulz, Josh Levy-Kramer, Camille Van Assel, Miklos Kepes and Nils Hammerla
A promising application of AI to healthcare is the retrieval of information from electronic health records (EHRs), e.g. to aid clinicians in finding relevant information for a consultation or to recruit suitable patients for a study. This requires search capabilities for beyond simple string matching, including the retrieval of medical concepts (diagnoses, symptoms, meditations, etc) related to the one in question. We open-source a novel medical concept relatedness benchmark, which is six times larger than existing datasets and consists of concept pairs that co-occurr in EHRs, ensuring their relevance for medical information retrieval from EHRs.
Published in 2020 | Coling 2020
Jonathan G. Richens, Ciarán M. Lee & Saurabh Johri
Machine learning promises to revolutionize clinical decision making and diagnosis. In medical diagnosis a doctor aims to explain a patient’s symptoms by determining the diseases causing them. However, existing machine learning approaches to diagnosis are purely associative, identifying diseases that are strongly correlated with a patients symptoms. In this paper we show that this inability to disentangle correlation from causation can result in sub-optimal or dangerous diagnoses. To overcome this, we reformulate diagnosis as a counterfactual inference task and derive counterfactual diagnostic algorithms. We compare our counterfactual algorithms to the standard associative algorithm and 44 doctors using a test set of clinical vignettes. While the associative algorithm achieves an accuracy placing in the top 48% of doctors in our cohort, our counterfactual algorithm places in the top 25% of doctors, achieving expert clinical accuracy. Our results show that causal reasoning is a vital missing ingredient for applying machine learning to medical diagnosis.
Published in 2020 | Nature Communications
Vitalii Zhelezniak, Aleksandar Savkov, April Shen, Nils Hammerla
Some of the top approaches to semantic textual similarity rely on various correlations between word embeddings, including the famous cosine similarity. We show that mutual information between dense word embeddings, despite being difficult to estimate, is another excellent candidate for semantic similarity and rivals existing state-of-the-art unsupervised methods.
Published in 2020 | ACL Journal
Stoilos, Giorgos and Juric, Damir and Wartak, Szymon and Schulz, Claudia and Khodadadi, Mohammad
The success of logic-based methods for comparing entities heavily depends on the axioms that have been described for them in the Knowledge Base (KB). Due to the incompleteness of even large and well engineered KBs, such methods suffer from low recall when applied in real-world use cases. To address this, we designed a reasoning framework that combines logic-based subsumption with statistical methods for on-the-fly knowledge extraction.
Published in 2020 | European Semantic Web Conference
Claudia Schulz, Damir Juric
We create various large-scale datasets for testing whether embeddings correctly encode the similarity between medical terms and test existing state-of-the-art embeddings on these datasets. Our results reveal that existing embeddings cannot adequately represent medical terminology. Our new datasets are thus challenging new benchmarks for testing the adequacy of new medical embeddings in the future.
Published in 2020 | AAAI 2020
Yura Perov, Logan Graham, Kostis Gourgoulias, Jonathan G. Richens, Ciarán M. Lee, Adam Baker, Saurabh Johri
The paper describes a probabilistic programming engine design and its analysis for counterfactual probabilistic programming, in general and in particular using importance sampling.
Published in 2019 | AABI 2019
Anish Dhir and Ciarán M. Lee
Knowing that a disease is highly correlated with symptoms, or a drug highly correlated with recovery, is not enough, and basing medical decisions on such information can be dangerous. To truly begin to revolutionise healthcare, AI must learn to distinguish cause and effect. Our work solves this by utilising new physics-inspired ideas about what it means for one variable to cause another, and showing how causal relationships in one dataset limit the possibilities in other overlapping datasets. To illustrate our algorithm, we apply it to breast cancer data, showing how to extract causal relations between two important features despite the fact that they were never measured in the same dataset.
Published in 2019 | AAAI 2020
Logan Graham, Ciarán M. Lee, Yura Perov
Provides efficient way to conduct counterfactual simulation, benchmarked against state of the art.
Published in 2019 | NeurIPS Causal Machine Learning workshop
Vitalii Zhelezniak, Aleksandar Savkov, April Shen, Francesco Moramarco, Jack Flann, Nils Y. Hammerla
We push the limits of word embeddings on semantic textual similarity tasks by introducing DynaMax, a novel unsupervised non-parametric similarity measure based on word vectors and fuzzy bag-of-words. This method is efficient and easy to implement, yet outperforms current baselines on STS tasks by a large margin.
Published in 2019 | ICLR
Gintaras Barisevičius, Martin Coste, David Geleta, Damir Juric, Mohammad Khodadadi, Giorgos Stoilos, Ilya Zaihrayeu
In this paper we report on our efforts and faced challenges in using Semantic Web technologies for the purposes of supporting healthcare services provided by Babylon Health.
Published in 2018 | ISWC
Douglas et al. Published in 2017 | NIPS Workshop, NIPS 2017

🗿 Big Rocks

Uniquely positioned to demystify and unify two critical trends in Healthcare

Value Based Care

Movement away from broken fee-for-service model
Aligns system around proactive care
Not scalable traditionally
Challenge addressing many patient types

Digital Health

Leverages technology-driven efficiencies
Bringing care to the patient vs patient to care
Not scaled to address holistic care
Shifts site of care vs addressing overall care and cost

🌎 Scaling Globally

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