How Will Machine Learning Impact Personalised Medicine?

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Dr Sobia Hamid has been working in the area of personalised medicine and machine learning undertaking scientific and commercial due diligence and marketing for venture capital, biotech and pharma companies. With over 12 years experience in both public and private health sectors, Sobia has research experience in Neuroscience during her Masters at Imperial College London, and in Genetics during her PhD at the University of Cambridge. In 2011 Sobia founded Data Insights Cambridge, a nonprofit with a community of over 800 data science practitioners - passionate experts working in academia, startups and corporate companies to solve the most complex data problems.

I spoke to Sobia ahead of her presentation at the RE•WORK Deep Learning in Healthcare Summit, in London on 7-8 April.

What areas of healthcare have the biggest potential for disruption by AI?
Results from early applications and demand in the market demonstrate that AI has huge potential to transform medicine and healthcare, and I believe we will see a significant impact within the next 3 years. Key areas showing the greatest potential are automated intelligent medical diagnosis, therapeutic recommendations and healthcare management. New AI technologies are already making inroads into helping to improve efficiencies, accuracy, and cost-effectiveness of medical practice, and will ultimately help us to move closer towards tackling challenging common syndromes, chronic illnesses and rare diseases.

What are the main risks associated with applying deep learning methods into healthcare and medicine?
Automating elements of medical practice means physicians will increasingly move away from traditional points of face-to-face patient-physician interaction. This should allow for improvement in quality of time spent with the patient, including more time spent on interpretation, communication and clinical-decision making. Automation of medical diagnosis needs to be accurate and avoid reporting incidental findings that are not backed by proven research. Otherwise the speed and convenience offered by medical AI risks negatively impacting lifestyle, health and reproductive choices if the information and recommendations are misinterpreted in the absence of human input to put these findings into context. Accuracy and reliability is also vital. It is important that medical AI technologies are tested rigorously to prove their purported benefits. Accuracy and reliability go hand in hand with ensuring data security and protecting consumers personal information.

What steps should be taken to try to mitigate these risks?
Establishing standards will stimulate the development, integration and adoption of AI technologies into healthcare. The ultimate aim is to utilise those technologies that are proven to be safe and improve patient outcomes. Medical professionals will also need to be trained to understand and work with these new systems, and medical education focus will need to shift to the areas where the input of human expertise is key. Finally, if medical AI is to achieve its fullest potential, we need to encourage international collaboration on AI, and share the benefits between developed and developing nations.

What impact do you think AI will have on personalised medicine?
Systems such as IBM’s Watson have the capability to analyse information in patient medical histories, laboratory data, genotype data, familial inheritance data, and biomedical research. With this breadth of in-depth automated data analysis and intelligent interpretation, it is possible to assemble individualized clinical and molecular profiles of each patient, and furthermore identify wider trends and associations in the data that can inform on individualised care. This is particularly relevant for rare diseases where complete individual patient profiles can be assembled and compared with similar cases around the world.

What area of deep learning advancements excites you most?
Deep learning technologies for medical imaging offer incredible advancements in improved resolution, breadth and speed of analysis and diagnosis. There are also sophisticated advancements being made across computational biology research, for instance in the application of deep neural networks to predicting protein structures. The application of deep learning methodologies to clinical trial data is also showing great potential for drug development and wider therapeutic interventions.

Sobia Hamid will be speaking at the RE•WORK Deep Learning in Healthcare Summit, in London on 7-8 April 2016. Other speakers include Alex Jaimes, CTO & Chief Scientist of AiCure; Brendan Frey, President & CEO of Deep Genomics; Ekaterina Volkova-Volkmar, Researcher at Bupa; Jeffrey de Fauw, Research Engineer at Google DeepMind and more.

Tickets are limited for this event, for more information and to register please visit the event page here.

Machine Learning Neuroscience Deep Learning Personalised Medicine Healthcare Deep Learning in Healthcare Summit MedTech


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