AI in Healthcare Day Two


Ai in Healthcare 2018 Day Two

08:00 Registration opens

Day 2 Keynotes - Science disrupters

Abraham Heifets

Using artificial intelligence to discover new medicines

Patrick Keohane

Using pharma data for machine learning

Daniel Ray

AI – a game changer in healthcare?

  • Effective use case examples
  • Implications for health data and technology
  • Opportunity for AI and value for the NHS
  • What is NHS digital doing in this space?
Theo Blackwell

Disruptive technology to improve the speed of clinical trials

Reserved for supporting partner
Please contact Derek Cavanagh or Alistair Wilmot if interested in sponsoring
Theo Blackwell, Chief Digital Officer, Mayor of London

10:40 Networking Break

Saman Farid

Break out panel: Investment into AI for Healthcare

  • Panel session lead by 6 senior investors, actively investing within the AI for healthcare space
  • What do investors look out for in start-ups?
  • What are the current trends in AI for healthcare?
  • Where do we see the industry moving to in the next 5-10 years?
  • How can you gain investment?

Machine learning and data

Patient monitoring

Chaired by Wei-Yi Cheng, Senior Data Scientist, Roche Innovation Centre New York

Chaired by Jim Fackler, Associate Professor, John Hopkins School of Medicine


Machine learning for big pharma big data

  • How machine learning facilitated the development of digital biomarkers for more objective, continuous assessment of Parkinson's disease progression
  • Opportunities of bringing value from medical images, genomics, EMR for drug development
  • The key challenges of internalizing state-of-art machine learning capabilities on pharma big data

Building a Cognitive Hospital - How Alder Hey are using Artificial Intelligence to develop a hospital capable of thinking, sensing and feeling in order to care for the people within it

  • The magical world of a "Living Hospital” and improving patient experience
  • Adapting emerging technologies to enable a building to care for those within it both medically and holistically
  • Building AskOli - a conversational interface for patients and families
  • Practical implications of using AI and overcoming barriers to adoption in the NHS

Making good decisions with Bayesian AI

  • Bayesian/probabilistic machine learning models can incorporate all sources of uncertainty, including noisy data and misclassified outcomes
  • This uncertainty is propagated to give a realistic assessment of the confidence in a prediction, leading to better decisions
  • Examples from safety pharmacology will be used to demonstrate the advantages of these models

Artificial intelligence for diabetes control

  • Type 1 diabetes sufferers rely on multiple daily insulin injections to control their blood glucose levels
  • There is a clear need for more automated and intelligent systems to support people with type 1 diabetes to manage their glucose levels
  • The latest advances in intelligent decision support systems for type 1 diabetes management developed at Imperial College London will be introduced
  • Results of our latest clinical trial evaluating an AI-enhanced artificial pancreas system will be presented

Transforming drug pipelines with real world data and machine learning

  • Value and insights in extracting insights from electronic health record (EHR) data populations for value-driven and precision medicine approaches
  • Advances in machine learning and deep learning techniques which have been successfully applied to EHR data resources
  • Overview of technical aspects, architecture and applications, - in particular those that will accelerate and impact pharmaceutical R&D

Remote GPs at your fingertips

Sunir Gohil, Clinical Artificial Intelligence, Babylon Health

Objective assessment of biological and chemical feature spaces towards drug combination synergy predictions using large-scale Oncology screening data

  • Assess biological and chemical feature spaces from a large-scale internal combination
  • screening panel towards identifying novel synergistic combinations
  • Discuss validity of the Machine Learning algorithm by assessing translatability to
  • other large-scale combination screens, as well as limitations of the approach
  • Discuss case studies across different modes-of-action mechanisms
Krishna Bulusu, Senior Scientist, Oncology Bioinformatics, AstraZeneca

AI at the bedside: from early recognition to value delivery

  • It is necessary but not sufficient to use “big data” and machine learning techniques to predict conditions best avoided (e.g. sepsis, hypotension, cardiac arrest)
  • It is further necessary but not sufficient to follow the above early detection with Augmented Intelligent Decision Support that more reliably delivers the correct therapies in a timelier manner
  • Decision support solutions must improve the value we deliver for the patient

13:00 Networking Lunch

Janette Rawlinson

Lunchtime break out panel: Patient involvement in precision medicine

Break out panel: Patient involvement in precision medicine
  • What are the benefits of involving the public in precision medicine
  • The patient experience with precision medicine and patient involvement
  • How clinicians can improve the patient experience
  • What are the barriers to involvement and how can they be overcome

Regulation and data for AI


Transforming healthcare delivery through artificial intelligence

  • History of Artificial Intelligence and Machine Learning and the applications within Health Care
  • An overview of Oklahoma State University’s (OSU) Center for Health Systems Innovation’s (CHSI) efforts to apply these approaches to the transformation of healthcare delivery with an emphasis on rural health care
  • Examples utilising the largest clinical database representing clinical information from 63 million patients collected over 16 years through electronic medical record systems

“Trust me, I’m a machine”: Public confidence and AI using patient data

  • Challenges for public, patient and clinicians’ confidence in the use of AI tools in healthcare and research
  • Research into public attitudes to ML and other data-driven technologies in health
  • What helps create a trustworthy system for using patient data – and what doesn’t

Machine learning in genomics and proteomics

  • See how is deep learning being applied
  • See how relationships in proteomics/genomics are being learned via network structure
  • See applications with potential clinical implications

Disrupting the invasion of AI in healthcare – examples from cardiology

  • Machine Learning and Digital Technologies have been common place in the management of patients with cardiovascular disease.
  • Innovations in cardiology bring convenience, improved outcomes and are soundly researched for safety and efficacy – examples of good practice
  • Security risks, employment impact of automation, alarm fatgue regarding events captured by monitoring technologies all pose issues
  • Does ML solve the human need condition or provide a lower cost, lower quality substitute to the vulnerable?
  • ML, digital technologies and healthcare professionals symbiosis – proposed meeting of minds

Multi-modal deep learned biomarkers of aging and disease

  • Using age as a feature to aggregate static and dynamic biological data
  • Assessing the biological relevance of the chronological age predictors
  • Using the biological age predictors for target identification and for clinical trials enrollment 
  • Using networks trained on age to generate the virtual synthetic patient data​

15:50 Networking Break

Closing keynotes – AI, pharma and healthcare working together

Mahiben Maruthappu

How AI has the potential to transform the NHS – and the limitations

  • How AI is being adopted in the NHS today
  • Key opportunities for the health service looking forward
  • How AI can be best introduced and adopted
  • Implementation and regulatory challenges for the system
  • AI: NHS friend or foe?
Panel discussion

Panel discussion: How can AI and pharma integrate to work together?

  • AI companies can access public data, however struggle to access privately held by pharma companies.
  • Will AI companies and pharma ever be able to work together to share this data?
  • How can we unlock the potential of AI in big pharma?
  • How AI companies can help pharma, and vice versa

17:35 End of Conference

last published: 19/Apr/18 10:55 GMT




Sign Up for Event Updates

World Precision Medicine Congress


Erica Baeta
+44 (0)207 092 1152


Joan Shutt
t/ +44 (0)207 092 1134


Issa Mauthoor
+44 (0)207 092 1257