AI in Healthcare Day Two


Ai in Healthcare 2018 Day Two

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?

Disruptive technology to improve the speed of clinical trials

Reserved for supporting partner
Please contact Derek Cavanagh or Alistair Wilmot if interested in sponsoring

10:40 Networking Break

Reserved for supporting partner
Please contact Derek Cavanagh or Alistair Wilmot if interested in sponsoring
Saman Farid

Lunchtime 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

AI and machine learning for data

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
AI and machine learning for data

Cognitive computing in pharma

Using AI for patient monitoring

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
AI and machine learning for data

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
Using AI for patient monitoring

Wearables to increase diagnostic accuracy

Reserved for supporting partner
Please contact Derek Cavanagh or Alistair Wilmot if interested in sponsoring
AI and machine learning for data

Cloud computing to ensure data safety and security

Reserved for supporting partner
Please contact Derek Cavanagh or Alistair Wilmot if interested in sponsoring
Using AI for patient monitoring

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

Regulation and data for AI

AI and machine learning for data

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 health care 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
Regulation and data for AI

“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
AI and machine learning for data

Applications of generative adversarial networks to drug discovery in oncology and infectious diseases

Reserved for supporting partner
Please contact Derek Cavanagh or Alistair Wilmot if interested in sponsoring
Regulation and data for AI

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
AI and machine learning for data

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
Regulation and data for AI

Panel discussion: Regulations in AI

  • How are we going to regulate AI for both healthcare and pharma?
  • Patient data and cyber security - should we be afraid of hacking?
  • Who should regulate this?
Victoria Cetinkaya, Senior Policy Officer, Policy & Engagement, Information Commissioners Office

15:50 Networking Break

Vivienne Parry

Breakout panel: Inside the ethics committee – the ethics behind using precision medicine

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?
Ed Addison

Value of AI in Drug Discovery

  • Machine learning for better tox prediction, pk prediction, enhancing binding preditction, reducing computational burden and assisting with target validation
  • Heuristic search of “molecular space”
  • Expert system for modelling medicinal chemistry thinking
  • Smart literature search for novel targets
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

18:00 End of Conference

last published: 16/Mar/18 13:45 GMT




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