Recap: Videos From Deep Learning in Healthcare Summit

On 7-8 April in London, we held the RE•WORK Deep Learning in Healthcare Summit, bringing together data scientists, CTOs, researchers and healthcare professionals for two days of discussions around the applications of artificial intelligence and machine learning in medicine and healthcare.

The summit showcased the opportunities of advancing methods in deep learning and their impact across healthcare, exploring how speech recognition, natural language processing, neural networks, computer vision can be applied to genomics, personalised medicine, diagnostics and more.

Attending companies included Johnson & Johnson, NHS, Imperial College, Google DeepMind, UCL, Accenture, Siemens, University of Cambridge, Bupa, as well leading academics and exciting startups in the field.


Artificial Intelligence in Improving Health Outcomes & De-Risking Clinical Trials
Alex Jaimes, CTO & Chief Scientist, AiCure

It’s very common for people to not take their medication as prescribed. In population health, medication adherence estimates are around 50%, while in Clinical Trials estimates range between 43% and 78%. This results in huge costs for the pharmaceutical industry—around $378 Billion dollars a year. In this talk, Alex gives an overview of why this matters, and describes how AiCure uses AI for medication adherence. The platform provides insights to physicians and clinical trial coordinators to produce better health outcomes and de-risk clinical trials, while encouraging patients or clinical trial participants to take their medication as prescribed, effectively impacting everyone on the planet.

How Will AI Help to Enhance & Personalise Healthcare?
Panelists: Cosima Gretton, Doctor, Guy's and St Thomas' NHS Foundation Trust; Reza Khorshidi, Program Lead, Machine Learning & Biomedical Informatics, The George Institute for Global Health; Mahiben Maruthappu, Senior Fellow to the CEO, NHS England; and panel moderator Nathan Benaich, Investor, Playfair Capital.

This panel discussion focused on the opportunities and challenges of using deep learning and artificial intelligence in healthcare and medicine, and explored vital questions like: what needs personalising in healthcare? What factors impact the integration of new technologies in hospitals? Can AI be used to empower patients and allow them to self-care more effectively?

The Data Delusion: Challenges for Democratising Deep Learning
Neil Lawrence, Professor of Machine Learning & Computational Biology, University of Sheffield

The widespread success of deep learning in a variety of domains is being hailed as a new revolution in artificial intelligence. It has taken 20 years to go from defeating Kasparov at Chess to Lee Sedol at Go. But what have the real advances been across this time? The fundamental change has been in terms of data availability and compute availability - the underlying technology has not changed much in the last 20 years. So what does that mean for areas like medicine and health? Significant challenges remain, improving the data efficiency of these algorithms and retaining the balance between individual privacy and predictive power of the models. In this talk Professor Lawrence reviews these challenges and proposes some ways forward.

View more videos from the Deep Learning in Healthcare Summit on our playlist here.

Our next Healthcare focused events include the Women in Machine Intelligence & Healthcare Dinner in London on 12 Oct and the Deep Learning in Healthcare Summit in Boston on 11 & 12 May 2017.

Future AI & Machine Learning focused events of 2016 include:
  • Deep Learning Summit, Boston, 12-13 May 2016
  • Machine Intelligence Summit, Berlin, 29-30 June 2016
  • IoT Meets AI Dinner, London, 15 September 2016
  • Deep Learning in Finance Summit, London, 23 September 2016
  • Deep Learning Summit, London, 22-23 September 2016
  • AI in Agriculture Workshop, London, 11 October 2016
  • Deep Learning Summit, Singapore, 20-21 October 2016
  • View all upcoming RE•WORK summits here.

    Neural Networks Machine Learning Deep Learning Algorithms Neuroscience Personalised Medicine Deep Learning AI Assistants Diagnostics Pattern Recognition AI Image Retrieval Healthcare MedTech Speech Recognition Genomics Deep Learning in Healthcare Summit


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