7-8 April 2016 London - domainreworkco.domain.com/DeepLearninginHealthcareSummit... · • BioBeats...

DEEP LEARNING IN HEALTHCARE SUMMIT

Summary Report

7-8 April 2016 London

Day 1 - Thursday 7 April

Daniel McDuff, Director of Research, Affectiva

Theme of presentation

Using ubiquitous devices such as smartphones, webcams, and smartwatches to measure human emotions and physiological signals at scale. Turning Everyday Devices Into Health Sensors. How can we use ubiquitous sensors to capture information about physiology and emotional state of humans.

Technology advancements

• Development of a large corpus of labelled data of facial expressions and gestures in unconstrained settings

• Deployment of sensor algorithms on mobile and cloud platforms • Physiology measurements (PPG, blood volume pulse, heart rate, heart rate

variability, respiration) using vision through webcam • Capturing physiological data (e.g. heart rate and respiration rate) using

accelerometer and gyroscope (cell phone, smart watch). Can also predict identity, posture and behaviour using these signals

• The collection of these measurements and their applications are both scalable

Applications for business

• Measuring social and emotional signals, and physiological signals such as blood flow, heart rate, and heart rate and respiratory rate variability

• Analytics on human emotions and physiological signals for healthcare, for example, assessment of peripheral artery disease and suicide risk

• Identification of people and understanding their behaviour over time, like for how long they are sedentary

• Discovering the variability in emotional expression across cultures • Measuring peripheral blood flow using cell phone cameras for diagnosing

Peripheral Artery Disease • Anticipate suicide risk analysing spontaneous facial responses rather than self-

reporting

Key take-away

Human emotion and behaviour can be captured and quantified at scale with mobile technology, and deep CNNs can learn useful representations of this information to improve human health. There are many opportunities to collect large amounts of health-related data with simple ubiquitous technology such as phones and wearables. This data can be then used for effective medical monitoring otherwise not possible or not scalable.

Davide Morelli, CTO, Biobeats


Predicting stress through physiological signals and teaching reduction strategies through a mobile app. The word in stress in modern society is not a medical term any more. Stress is the flight-or-fight response of our body that we go through when we are facing a challenge. Stress can be both good and bad, but prolonged stress without recovery is dangerous. Autonomic activity of the central nervous system and stress state affects heart rate variability (HRV), so it can be used to estimate balance in the autonomic nervous system. A complication is understanding HRV for different people of different gender, lifestyle and physiology.


• Development of a cardiovascular data set of real-world • Individualised stress models rather than a one-size-fits-all • Machine learning SDK and API for data acquisition, processing, and analysis • Infrastructure for data collection, analysis, and visualisation of stress data


• BioBeats offers a mobile-based cardiovascular epidemiology monitoring and self-treatment training

• Quantification of stress and gamification of treatment • Analysing workplace performance and health • The app can display various logged physiological signals through a dashboard. • Self-treatment: the app can teach users how to deep breath. • Music: can be played to sync with physiology (also to make users move more). • “Hear and now” app that apples biofeedback to teach deep breathing • With large amounts of data it is possible to personalise models (meaningfully

simplify them for each user) and can define baselines of stress for users. • The user can be entertained while data is gathered, increasing user retention. • An important future application will be the collection of continuous data for

events using wearables to predict behavioural patterns.

Key take-away

Modelling individual differences and biases is important for accurate stress models, and user feedback for this can be easily collected via mobile platforms. Individuals can use the insight from this data to increase their quality of life. Stress is loosely defined in modern society, but still needs to be monitored and taken care of. Large amounts of physiological data can be collected through wearables and mobile devices. This data can be used to train models to analyse and monitor the physiology of users as well as their behaviour.

Brendan Frey, President and CEO, Deep Genomics


Deep Learning for Genomic medicine. Instead of detecting cats, we should make profound impact and help people. The value is in reducing uncertainty and really understanding the data.

Deep Genomics uses neural networks to read the genome. A challenge is the genotype-phenotype gap, i.e. the genome doesn’t affect the phenotype directly, but through a complex network of biological pathways through RNA, proteins and metabolites.

The hypothesis is that we collect lots of data and then understand the relationship between genotype and phenotype. But large numbers of data are nit sufficient to close the gap. There is an exponential data problem and too much data would be required to make revealing associations with genome-wide association studies (GWAS).

We need inductive learning to tackle the exponential data problem. We need intermediate molecular phenotypes (proteome, metabolome, chromatine structure, etc ) as stepping stones to connect genotype to phenotype.

In order to gain trust in the results, we need to achieve high clinical accuracy and causal biological explanations, in other words: reasoning.


• Deep Genomics works with a large pool of data and developed Deep Bind, a CNN for predicting protein-DNA and protein-RNA interactions. They also developed Spidex, which scores genetic variants predicting splicing and disease.

• DeepBind: detecting whether protein binds to a DNA sequence (state-of-the-art).

• Spidex: Deep neural network that scores genetic variants by their predicted impact on splicing and disease.

• Use of neural networks to provide causal, explanatory, noninvasive, and context-incorporating models


• Hypothetically, the inferences and findings may be used for researching longevity – some animals live much longer than humans while not having very different metabolism, hence it should be possible to make a profound difference in the death risk curve over life time. Neural Networks can be used to learn causality between each node in a biological network of pathways between phenotype and genotype.

• Current the systems are used to learn about Spinal Muscular Atrophy using protein phenotype, with data gathered from mutation experiments. It is now possible to predict risks for a child.

• Another present application is to improve the power of GWASs, e.g. for autism.

• In the next five years, using neural networks to figure out how to read the genome and understand molecular phenotypes in more detail

Key take-away

Conventional approaches to genomic science are inadequate because of the exponential data problem. Deep learning, however, can be used to narrow the mismatch between the volumes of genomic data we possess and our inability to understand them, and this will create value by helping people plan for negative outcomes. Machine learning in health requires “super-human AI” as humans are not built to understand the genome, in contrast to text, speech, images, etc. Advances in machine learning are required, not more data. We need super-human AI to understand genetics.

Diogo Moitinho de Almeida, Senior Data Scientist, Enlitic


The shortcomings of current deep learning tools such as Theano, Torch, and TensorFlow. Artificial NNs are extensible and modular but have a number of limitations and drawbacks that may limit our thinking. The science is there, but there are a lot of tricks to use in DL to make it effective.

Summary

Deep learning is excellent for medical applications because of its modularity, reproducibility, and potential ease of use. In practice, current deep learning tools are not flexible enough in that it is hard or impossible to implement an array of tricks like nodes with non-differentiable updates, costs with parameters, gradient clipping and noise, unsupervised pertaining, alternative optimisers, and stochastic depth.

Some of the problems are: • optimisation • efficiency (parameter and data efficiency, algorithms) • creativity – research in improvements • flexibility – real world limitations, i.e. the models that we can build are very

limited

Technology Advancements • Learning windowing for medical imaging and arrive to the same ranges that

humans use. • GradNets: Dynamic Interpolation between Neural Architectures

Applications for Business

• Combine image and genetic information to do personalised medicine. • Localise and classify cancer in super-human level.

Key take-away

To make deep learning methods accessible in domains such as healthcare and perform at the start-of-the-art we need more flexible modelling tools for easily incorporating the special cases and tricks of the trade. We must be able to partition easily not only the problem but the solution

Jackie Hunter, CEO, Stratified Medical


Changing the drug discovery paradigm. Most drug R&D people work on compounds don’t make it to market, but even they do, it can take 15-20 years at a very high cost.


• Collaborations with, for example, the Crick Institute and UCL in the “Knowledge Quarter” of London

• A knowledge graph for leaner drug discovery • The Ferret Chrome extension (http://ferret.ai/) for summarising/enriching the

browsing of scientific articles and providing full-text search on your history


• Quicker time-to-market and reduced cost for new drug development • Repositioning drugs • Making fewer compounds, but the right ones. • Understanding dosage effects • Finding the right patients • Overall, dramatically reducing time and financial costs throughout the drug

design pipeline.

Key take-away

There is an urgent need to move to away from the old paradigm for drug discovery that is slow, expensive, and risky. A new paradigm is possible, using the insight from machine learning and heterogeneous data sources to more efficiently identify the right targets and compounds. Stratified Medical is aiming to have a system-level impact on the healthcare by reducing drug discovery and drug costs and accelerating development through improved success rates and development speed.

Alejandro Jaimes / CTO & Chief Scientist, AiCure


Automated enforcement of medication adherence through mobile apps. Artificial Intelligence in Improving Health Outcomes and De-Risking Clinical Trials

Adherence means taking medication in the right time and the right way.

People are bad at taking medication. They say they are taking the drug, but they might not, or not well. Poor health leads to high noise and it is impossible to know when looking at the data, leading to high costs and lower accuracy, with lower success rates.

Failure to take medications also leads to a large number of deaths in hospitals (about 10%). Adherence impacts: patients, doctors, insurance companies, pharmaceuticals, governments. Adherence is usually around 43%-78% adherence in clinical trials and ~50% in hospitals. Surveillance is an inherent, very important part of clinical trials.


• A medication adherence tool for mobile platforms that provides real-time analytics to doctors and researchers

• Computer vision algorithms (facial recognition, identification of medication, confirming ingestions) for confirming medication adherence and detecting fraud

• Adapts to patient behaviour • Can be personalised, tailored to diseases


• Surveillance and fraud monitoring for medical professionals and researchers • Improve clinical trial outcomes by reducing noise in clinical trials. • AICure reduces non-adherence and dropout rates from ~40% to ~30%. • Population health: drug abuse monitoring, increases adherence, reduces

patient risks and population risks (e.g. taking TBC drugs).

Key take-away

Adherence to drug taking instructions is crucial for the reliability of clinical trials and for population health and reducing healthcare costs more generally. Purely software solutions can enforce medication adherence in a scalable fully-automated manner, and the continuous collection of data permits immediate interventions

Ben Glocker, Lecturer, Medical Image computing, Imperial College London


Deep semantic understanding of medical images. Discovery and understanding can be used for population modelling. Personalised medicine and decision support can be used to move towards computer-aided diagnosis.


• DeepMedic (https://biomedia.doc.ic.ac.uk/software/deepmedic/) software for brain lesion segmentation

• The technology pipeline consists of extracting information, segmentation and modelling.

• Currently it is possible to do advanced Foetal Imaging and Diagnostics, intelligent imaging of the heart, semantic imaging, or detecting and tracking a guide wire during surgery.

Research overview

• Advanced foetal imaging and diagnostics • Understanding brain development • Intelligent imaging of the heart • Motion tracking and correction for MRI • Real-time SLAM (simultaneous localisation and mapping) and augmented

reality • Whole body imaging and segmentation of organs/body parts (that is,

semantic imaging)


• Decision support in diagnosis, therapy, and intervention • Patient stratification in clinical studies • Population and disease modelling • Discover of imaging biomarkers • DeepMedic is a tool developed using deep learning for brain MR traumatic

lesion segmentation and volume estimation & tumour tissue classification. • Data management, mining, retrieval • Automated QC

Key take-away

Deep CNNs can learn useful representations of medical images, for example to detect white matter, grey matter etc. Significant advances have been made through deep learning, however it mostly requires labelled data. Some research challenges are how to use weakly, incorrectly or unlabelled data, providing confidence estimates, interpretability of predictions (doctors don’t like black boxes!)

Neil Lawrence, Prof. Machine Learning & Computational Biology, Uni. Of Sheffield


The data delusion: progress is not being driven by machine learning. Alpha Go saw lots of data, far more than any human could do in a lifetime. DL doesn’t work on small datasets, so we need to be careful about what method to actually use in practice. The reason is that we can never tell how a DL model will work with unseen data. It is also hard to deal with missing data – we need to deal with uncertainties in a probabilistic way.

Most of the data is at Google, Facebook, Amazon and other large players. These companies are happy to share algorithms, because startups can not do anything with it without data

Projects/Companies

• CitizenMe (http://www.citizenme.com) “Take control of your personal data. And realise its value.”

• CitizenTrust (http://www.citizentrust.org/) “We believe the web should be democratic. It should serve us all equally.”

Key facts

• DeepFace: requires 1 billion face images


• DL models are inefficient. It may be a better choice to use alternatives to DL, such as Gaussian Processes (GP). The benefit of GPs is that: • it can throw away all data that doesn’t fit the model • it works with analytical integrals • Currently there are some challenges in scaling GPs.

Key take-aways

• Current deep learning model are deeply inefficient in that they require masses of data to learn. A major challenge is to do machine learning with the paucity of information with which humans manage [Personal note: see the following recent paper on this issue, http://arxiv.org/abs/1603.05106].

• Most deep learning models are non-probabilistic; we must develop deep probabilistic models more fully to deal with the uncertainty inherent in health.

• We must also consider carefully how to do distributed machine learning in a way that does not require people to relinquish control of their personal data

• The next important step would be to be able to use machine learning with small data (e.g. on the scale that humans can work with) and data infrastructures have to be improved.

PANEL – How will Artificial Intelligence help to Enhance and Personalise healthcare

• Cosima Gretton, Guy's and St Thomas' NHS Foundation Trust • Reza Khorshidi, The George Institute for Global Health • Mahiben Maruthappu, NHS England • Moderator: Nathan Benaich, Playfair Capital

Themes

It is important for the whole patient pathway for every disease state that we move away from one-fits-all care to personalised medicine.

The data for personalisation will come from personal genome sequencing, personal data from wearables and the AI that can allow for to understand and meaningfully use this data.

Unfortunately an important pillar of personal medicine, pharmacogenomics, is far from getting usage in healthcare. The currently reality is that most of the pathways are far from being ready for adopting new technologies, let alone personalisation. It is like trying to bring the digital age into the stone age. The small degree of personalisation that exists and is being applied currently is rather disappointing than helpful.

We also need to redesign the currently reactive healthcare to predictive healthcare. The positive trends are moves towards episodic and proactive healthcare from reactive. AI can help in empowering healthcare employees to do better, more impactful and more interesting work and up-skill people.

The whole of the NHS is under strong pressure –taking out time to adopt new technologies is challenging at best. People are also used to simple models and tools that they understand, which will make it even harder to apply AI and get any traction. People abandon the technologies very quickly.

There is also major tension between consistency and personalisation. Most challenges are organisational and political. It is also important to not disrupt parts of healthcare that we can’t.

Ali Parsa, CEO, Babylon Health


The democratisation of healthcare via mobile platforms. Babylon wants to make healthcare accessible and affordable everywhere in the world, just like Google made information available to everyone. Babylon wants to bring healthcare to homes and mobiles from clinics and hospitals. AI may be the first tool that can help us achieve accessible and affordable healthcare.


• Dialogue expert-system for querying symptoms • Probabilistic models to reconcile the uncertainty from multiple symptoms to

produce a diagnosis • An infrastructure for end-to-end medical care, from booking a GP

appointment, FaceTime with a GP, ordering medicine, receiving reminders to administer them, summary statistics from your vital health measurements etc.

• Mobile healthcare assistant • does Q&A with user with appropriate follow-on questions • does auto diagnostic • can set up in-phone consultation with Babylon’s doctors and can rate the

doctor after consultation • the app generates notes for the user • the app guides users to a near-by pharmacy

• the app automatically initiates follow-up • the app produces a self-administered healthcare plan recommendation • an analytical system can predict anomalies, e.g. risk of suicidal tendencies

from depression signs


• AI for medical diagnosis (using speech recognition and a dialogue system on a mobile app)

• Automation in the processes of medical care and treatment

Key take-away

Babylon Health aims to make healthcare affordable to everyone on earth, just as Google has made information universally accessible. This is absolutely possible using AI and mobile technology.

Michael Nova, CIO, Pathway Genomics


AI for personalised, actionable healthcare advice. Healthcare is becoming prohibitively expensive. Cognitive AI healthcare is necessary.

There is a huge amount of data being generated constantly, currently doubling every 3 years, but lot of the data is incorrect or noisy. Healthcare workers are concerned and need guidance in what to do with all the data.

For instance, obesity is becoming the highest risk factor for cancer in the US and people need personalised recommendations about eating and exercise. Everyone is going to be gene sequenced in the coming years in the US, with insurance paying for it.


• “Ome™ is the world’s first precision health and wellness mobile application using artificial intelligence and genetics.” It gives specific, actionable information.

• IBM Watson aims to use cognitive computation with Bayesian Inference. In general, Watson is not good at personalisation, but Pathway Genomics aims to build personalisation on top of Watson, mainly using classical methods including regression, decision trees, SVMs. Their mobile app assistant OME incorporates a lot of genetic and medical information to give personalised recommendations and advice.


• Automatically extracting information from unstructured medical data (amongst other more traditional sources)

• Learning from user feedback and preferences • Producing actionable insights and outcomes from models

Key facts

• A person generates 1TB of medical data across his/her lifetime • Medical data doubles every three years • Data analytics around preventative health measures could save $300 billion in

US

Key take-away

The analytical insight provided by AI will revolutionise healthcare by providing personalised medical council. Nonetheless, to turn these insights into action we must consider human behaviour, and use an understanding of that to create an engaging and persuasive app (for instance, by backing up the call-to-action with an evidence-based justification). Engagement is important for users, and evidence-based medicine is important for the quality of care.

Day 2 - Friday 8 April

Ioannis Katramados, Founder, COSMONiO


Anomaly detection in radiological images using deep learning Technology advancements

• Nous GPU cluster as a service platform for deep learning • Deep learning software for embedded GPUs (iPad Pro)

COSMONiO has done a number of projects around deep learning. From analysing toxicity to learning to focus a lens. They were helping experts to use deep learning. When the decision came to become a consultancy or a product company, they started to develop their own solution called NOUS. A piece of hardware that allows any expert to train deep neural networks. Combined with an app for the iPad Pro, they want to help radiologists to label medical images and train networks to detect anomalies.

Technology Advancements

• DeepFocus: 99.9% accuracy to focus a lens • DeepTox: analyse healthy/non-healthy strips • Radiologists missed gorilla in chest CT. Focus on wrong part of the image. • Automated image ranking: ranked by risk factor of having an anomaly. • Petridish tapping on iPad to label data for future training. • NOUS: hardware solution to help experts train their own model. Active Deep

Reinforcement platform. • Anyone can train DNN…


• Augmented intelligence for analysing medical images

Key take-away

Deep learning models require a volume of data and there may be a dearth of medical scans for rare conditions. Idea: Learn a model on the plentiful healthy individuals then detect illness as an aberration. This can be used to speed up the work of radiologists by showing areas with a high probability of anomaly. COSMONiO believes deep learning will become a commodity available to everyone.

Mohamed Sayed / Founder & CEO, Heuro Labs (http://www.heurolabs.com/)


What does Heuro Labs do? Makes sense of the world information and enable cognitive computing. From data to intelligence.

How do we make sense of the world’s information and use that to enable cognitive computing? The same person can have different data in many different contexts. Heurolabs wants to produce meaningful technology that has a positive impact on people’s lives. Heurolabs is building a platform that gets meaning from visual, auditory, textual and numerical data. This can be accessed through API calls or complete solutions.


• Pipeline framework for flexible machine learning • CognitHealth: summary dashboard of health statistics • Clinical feedback from unstructured text


• Blood film processing to detect infected cells, their location, and count of healthy cells

• Extracting user attributes from unstructured clinical feedback data • Oncology imaging

Key take-aways

• Data => information => knowledge => wisdom • Heuro Labs aims to allow you to extract that wisdom with an API based on the

abstraction of pipelines • Existing platforms are not well suited for organisations that can not spend a lot

of money on computing infrastructure.

Alejandro Vicente Grabovetsky, Co-founder, Avalon AI


Detecting dementia before it happens, One in 3 people get dementia. The early stage of Dementia is Mild Cognitive Impairment (MCI). Progression from MCI to Dementia can take up to 10 years.

Key facts • 1/3 of people die with dementia • Healthcare costs £26B in UK • There is no cure for dementia • 99.6% of dementia trials failed because they targeted late stage => early

intervention is the key


• Model that achieves 75% accuracy in predicting transition to dementia from MCI

• Conglomeration of 70,000 brain scans from universities and hospitals


• Reducing cost of early intervention dementia trials • Selecting the patients for clinical trials, by finding those who will deteriorate to

Dementia soon, so as to shorten clinical trials.

Key take-aways

• Deep learning models on brain structure can be used to predict a trial candidate’s probability of transitioning from MCI to dementia, and this information can be used to reduce the cost of early stage dementia trails.

• It is hard to transition from shallow models such as SVM to deep models such as CNNs because of the paucity of data.

• Existing deep learning frameworks have an academic bent and may not be well suited for all industry application, for example, analysing single channel 3D images

Eduardo W Jorgensen, CEO, MedicSen


A mobile app for diabetes medical care, Diabetes is one of the greatest health risks of our generation. MedicSen aims to decrease hypo & hyper glycemia, simulating the physiological function of the pancreas. Recurrent neural networks are able to find patterns in temporal blood sugar level measurements.

Key facts

• 415 million people have diabetes • 5 million die of it each year • $700B is spend on treatment • Interesting observation: RNNs tend to deal worse with hypo- rather than hyper-

glycaemic event prediction


• Mobile app to provide continuous monitoring, intelligent management, and personalised therapy

• Model for predicting abnormal glycaemic events • Deep learning to train patterns and feed that into classifier


• Real-time collection and analysis of glucose information for diabetics, uploading to cloud and communicating to doctor

• Of deep learning more generally in the future • automatic pattern detection • therapy design and treatment planning • molecular biology of disease studies • robotic driven automatic surgery • screening and diagnosis in E.R.

• blood tests

Key take-away

• The problem is hard because of the paucity of data and high dimensionality of feature space (interesting events happen very rarely)

• How ought we manage the doctor/patient relationship in digital form? • Medical education needs to adapt to incorporate training in AI methods • The law must adapt to deal with issues around data privacy/security etc. • Multidisciplinary teams are absolutely necessary to succeed as a technology

company in healthcare.

Riley Doyle, CEO and technical lead, Desktop Genetics


Optimising CRISPR genome editing with machine learning. CRISPR/Cas9 is an inexpensive way to engineer genomes of animal and plant cells. How can technology help to design a good gene engineering experiment? Desktop Genetics is building a platform for altering genome at a precise location with high specificity in silico. The platform allows for finding the best guiding RNA sequences useful for gene editing.


• Deskgen platform for start-to-finish gene editing from your computer Applications for business Gene therapy for degenerative blindness, swine fever resistance, HIV cure

Key facts

• Deskgen outperforms manual design in CRISPR success rate by a factor of 3

Key take-away

Machine learning can be used to automate the process of gene editing with CRISPR. Challenges remain including the high dimensionality of the data and paucity of samples. Shallow GLMs current perform better than deep NNs for these reasons.

Alex Zhavoronkov, CEO, Insilico Medicine


Application of deep learning to ageing research. Insilico Medicine’s goal is to solve ageing and to extend productive longevity. They have build technology to estimate your age based on a blood sample. The framework consists of an ensemble of 21 neural networks. When the age estimate from the blood sample is decreasing, it can help assess the efficacy of anti-ageing drugs.


• Prediction of ageing by blood test information (http://www.aging.ai/) • First AI judged beauty contest (http://beauty.ai/) • Ensemble of 21 DNNs • Trained the model on faces to learn the features of beauty in partnership with

a cosmetics company


• Discovery of disease and ageing biomarkers

David Clifton, Associate Prof of Engineering Science, Oxford


Augmenting intelligence of medics by finding salient data, the needles in the haystack. The next generation of health informatics is mobile. Sensors in mobile devices will be the biggest source of healthcare. Sensor data is very noisy. How to deal with that in a principled, robust way? Non-parametric bayesian methods are key to perform principled ML inference. We must learn about the practical performance of various available algorithms and perform principled Bayesian fusion.


• How to perform mobile monitory robustly (deal with sensor failure, missing data)

• Bayesian methods and non-parametric methods. • Early warning scores for nurses


• Early indicators of illness based on sensor readings • Monitoring health of underground water sources in developing countries such

as Kenya • Predicting groundwater depth from hand pump accelerometer data

Key take-aways

• How much machine learning is impacting clinical practice in e.g. NHS? Not very much.

• Shallow probabilistic models such as Gaussian processes have advantages over deep supervised methods in being able to incorporate prior clinical knowledge, deal with uncertainty in a principled way, and be robust to noise and artefacts [Personal note: Deep GPs have been explored recently]

• Augmenting human intelligence by bringing 10% of salient data to expert’s attention. Data fusion using Bayesian methods.

http://www.aging.ai/

http://beauty.ai/

Alex Matei & Ekaterina Volkova-Volkmar, Bupa


Understanding human behaviour using machine learning to overcome addiction. How can we use machine learning for behaviour change? In this talk we heard how Bupa has built a system to predict smoking episodes as part of the smoking cessation intervention programme called Bupa Quit. How do you decide on the correct data representation for deep learning? The researchers at Bupa looked at stream data, e.g. daily logins to Bupa quit, and click paths with the app.


• Bupa Quit app to assist smoking cessation through clinically proven behaviour change interventions (e.g. show people how money they are saving by not smoking, offer people activities when they report a craving)

• Model for predicting users next smoking episode (and making a proactive intervention)


• Insight into user behaviour through data analysis, for example, discovery that most app users start attempt to quit on Monday and most smoke their first cigarette in the first half hour of waking

• Clustering of users by behaviour

Key facts

• Bupa is a private healthcare company with over 29 million customers and 84,000 employees

• Tobacco use is projected to kill 1 billion people in the 21st century • Cessation before middle age avoids more than 90% of lung cancer risk • Model achieves around 85% accuracy in predicting a smoking episode with the

first 3 days

Key take-away

The insight from AI and elements of gamification can be used to monitor and increase the effectiveness of medical apps intended to change human behaviour, such as breaking addictions.

Randeep Sidhu, Head of Product, Your.MD


The democratisation of healthcare via mobile platforms. Structural inefficiency in healthcare. Hope is that mobile will save that. Problem: doctor is the only source of trusted information. Lack of doctors in the developed world. Widening supply and demand gap. There is a structural inefficiency in healthcare. Your.MD

(Your Mobile Doctor) believes that mobile technology will fix that. The problem is that the doctor is the only trusted source of information and there is a lack of doctors. Even in the developed world there is a widening supply and demand gap. Your.MD’s AI personal health assistant allows people to take control of their own health by using NLP and a chat interface to listen. Diagnoses are made using bayesian networks. A value network predicts what you are suffering from and a policy network determines what to ask to get more of the right information.


• Your.MD: an AI personal health assistant to help people take control of their own health

• Map of conditions of relationships between conditions and symptoms. • Chat bot using NLP and free form interaction (rather than standard questions)

for diagnosis • Integration of chat bot with Slack, Telegram etc., which has increased uptake

with young • Integration of trustworthy information from NHS, written by journalists and

verified by doctors

Key facts

• Takes 7 years on average to train a new doctor • Over half of the world’s population doesn’t have access to healthcare • “Demand for physicians continues to grow faster than supply”

Key take-aways

• There is a structural inefficiency in medicine that can be fixed with AI and mobile technology

• Doctors in most case only disperse information • Your.MD helps you discover the cause, understand what to do, and help you act,

reducing the information asymmetry between doctors and the public • It is not intended to replace doctors, but improve accessibility and reduce

burden for minor consultations. • There is a widening supply and demand gap for doctors. Mobile technology

can bridge that gap by offering patients the information they need.

Vinay Kumar, Founder, Arya.ai


Multi-functional deep neural nets in automating primary healthcare. Arya.ai uses multi-functional deep learning to automate primary healthcare by means of AI bots, computer vision, speech, and reasoning. Their systems have achieved 83% accuracy for image classification of diabetic retinopathy. Moreover, they are working on a coding language for AI to easily build and train models. Different models can be combined to build your own bots. All of this can be done through Arya’s API.


• Arya: a multi-functional platform for deep learning with computer vision, natural language, and other more general types of AI


• Reducing the barriers to entry for other companies to apply AI • Computer vision for radiology => automated diabetic retinopathy, MRI, CT, and

pathology reports • Human action tracking • Natural language understanding • A research companion for finding answers, recommending methods etc.

Key take-away

The basic building blocks of deep learning can be combined in many ways to build successful AI agents in a range of domains. Thus there is value in creating an infrastructure to simplify exploiting this modularity.

Authors: Stefan Webb, University of Oxford; Tobias Rijken, UCL; Peter Kecskemethy, University of Oxford.

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