Over the past decades, healthcare has slowly become to rely upon and seek assistance from technology, in varying complexities. From computer-assisted breakthrough in labs, to the introduction of online patient records, hospitals and general practices as well as research have realised the necessity of digitalising operations. Research and rolling out new treatment methods is extremely expensive, but the advent of AI introduces the possibility of self-running engines which can potentially create $150 billion in annual savings for the US healthcare economy by 2026, according to Accenture. AI in healthcare will be able o augment human behaviour and can help in a variety of tasks from administrative and clinical healthcare functions, to drug discovery and diagnostics.
At the Deep Learning in Healthcare Summit in London this September 20 - 21, RE•WORK will be bringing together key players in AI and healthcare to discover the deep learning tools & techniques set to revolutionise healthcare applications, medicine & diagnostics. Confirmed speakers include Sarah Culkin, Strategic Data Lead at NHS England, Graeme Rimmer, Engineering Manager at Google, David Clifton, Associate Professor of Engineering Science at University of Oxford and many more. In advance of the summit, we're taking a look at 5 emerging applications of AI in healthcare.
Traditionally, estimation of the underlying genetic propensity for certain traits or diseases has been based on the use of single genetic variants identified to have a large effect on the outcome of interest. However, in the past 10 years, it has become increasingly clear that most complex traits are driven by a large number of genetic variations with small effects. As a result, polygenic scores (PGS) have become the focus of efforts to estimate genetic propensity. PGS are typically defined as a weighted linear combination of many small effect genetic variations. Thus, PGS do not take into account higher order interactions between variations, non-linear effects or interactions with environment. Practically, addressing these issues using standard statistical modelling techniques can be cumbersome. Neural networks provide an alternative and flexible paradigm by which to evaluate the efficacy of non-linear variations on PGS.
At 23andMe, the AI team are working on developing the next generation of the company’s consumer health platform. This R+D team develops predictive models for human traits and diseases that incorporate genetics, lifestyle and environment.
Watch a video presentation from Nick Furlotte, Senior Scientist at 23andme.
Medical imaging has come a long way from the early days of CT scanners and mammography devices. With 3D medical imaging, healthcare professionals can now access new angles, resolutions and details that offer an all-around better understanding of the body part in question, all while cutting the dosage of radiation for patients.
“Modern radiology is completely dependent on 3D visualization,” says Dr. Frank Rybicki, professor and chair of the University of Ottawa’s radiology department and chief of medical imaging at The Ottawa Hospital. “It’s part of the culture of radiology at this point.”
In addition to volume, 3D medical imaging provides a clearer picture of blood vessels and crisper images of bones.
Read more from HealthTech.
Earlier this year, Mediclinic City Hospital in Dubai, U.A.E. conducted the first robotic-assisted knee surgeries in the Middle East, using Artificial Intelligence (AI) technology to conduct the partial and total knee replacements on two patients. The private hospital, which is part of Mediclinic International, is one of many regional healthcare facilities taking part in a growing trend of investment into future AI tools that will add precision and accuracy in surgery.
The uptake in the use of cutting-edge surgical robots and the increasing application of AI technologies in healthcare has been very visible, particularly in the U.A.E., since the launch of the U.A.E. Strategy for Artificial Intelligence in 2017; a major initiative within the U.A.E. Centennial 2071 objectives. The strategy aims to make the country a world leader in the field of AI investments in healthcare in order to minimize chronic and dangerous diseases. The U.A.E has also appointed its first AI minister to implement this vision.
Read more from Forbes Middle East.
An enormous figure looms over scientists searching for new drugs: the estimated US$2.6-billion price tag of developing a treatment. A lot of that effectively goes down the drain, because it includes money spent on the nine out of ten candidate therapies that fail somewhere between phase I trials and regulatory approval.
Leading biopharmaceutical companies believe a solution is at hand. Pfizer is using IBM Watson, a system that uses machine learning, to power its search for immuno-oncology drugs. Sanofi has signed a deal to use UK start-up Exscientia’s AI platform to hunt for metabolic-disease therapies, and Roche subsidiary Genentech is using an AI system from GNS Healthcare in Cambridge, Massachusetts, to help drive the multinational company’s search for cancer treatments. Most sizeable biopharma players have similar collaborations or internal programmes.
Read more from Nature.
The Department of Veteran Affairs (VA) and IBM Watson Health have announced an extension of their partnership to bring artificial intelligence and genomic analytics to cancer care.
The VA’s precision oncology program primarily supports stage 4 cancer patients who could be eligible for alternative treatment options. With precision oncology, providers can identify the specific genetic influences involved in cancer and choose therapies that will specifically target the condition.
“Our mission with VA’s precision oncology program is to bring the most advanced treatment opportunities to Veterans, in hopes of giving our nation’s heroes better treatments through these breakthroughs,” said Acting VA Secretary Peter O’Rourke.
Read more at Health Analytics.
Interested in learning from leading experts in the space? Join us in London this September.