Cloud based company, Arterys, has created an intelligent medical imaging platform that supports advanced AI tools and ultra-fast image processing. Back in 2011 when Arterys was founded, most medical imaging happened on hospital premises with limited computing power, with measurements taken manually and automated tools minimal. Out of this, the vision to advance medical imaging via cloud computation and advanced analytics was born.
Last night in London we were joined by guests eager to support women working in AI for an evening of drinks, informal talks, networking and a three course meal. We discussed the potential of local computations on wearables, the challenges and solutions to algorithmic fairness, and the applications of deep learning via image recognition to improve society. Find out what you missed in our summary of the evening.
AI is transforming the healthcare industry and has the potential to disrupt and improve the way we discover drugs and how we do biomarker discovery. This week, Insilico Medicine announced in their paper ‘Converging blockchain and next-generation artificial intelligence technologies to decentralize and accelerate biomedical research and healthcare‘ their most recent breakthroughs, bringing together AI with blockchain technologies to give you access to your own medical data to ensure that you have total control and ownership of your own data to ‘redefine how pharma is done’.
Insilico Medicine are bringing to market a ‘secure and transparent distributed personal data marketplace utilizing blockchain and deep learning technologies may be able to resolve the challenges faced by the regulators and return the control over personal data including medical records back to the individuals.’
Looking back at the attendee feedback from the first half of the year, it's been a truly eventful 6 months. We have met so many attendees from different backgrounds and expertise, all coming together to discuss the advancements of deep learning, machine intelligence and more.
Since we first met the team of diabetes treatment startup MedicSen at the 2016 Deep Learning in Healthcare Summit in London, their team has grown and their mission has evolved. We spoke to the CEO Eduardo Jorgensen to learn more about their startup journey over the last year, and to find out what we can expect next.
Radiology requires countless hours searching for tiny lesions, creating distance and contour annotations, and filling out checklists to determine stages of disease - these tasks are onerous and error-prone, resulting in high costs and frequent misdiagnoses. Thankfully, the global impact of deep learning is now removing the tedium for radiologists. We spoke to Dan Golden, Director of Machine Learning at Arterys, to learn more.
With medical imaging accounting for approximately 90% of all medical data, the application of artificial intelligence to images for more efficient and accurate diagnosis could be a real game-changer. However, the technology is still relatively new, the challenges are to be expected. Ben Glocker, Lecturer in Medical Image Computing at Imperial College London, discusses some of the successes and draw-backs in applying deep learning to medical imaging.
Last Tuesday, the Deep Learning in Healthcare Summit London took place at LSO St Luke’s. RE•WORK hosted 40 speakers and 200 attendees over the course of the 2-day summit to explore the latest developments and applications of Deep Learning (DL) within healthcare, medicine and diagnostics.
Although deep learning models are giving increasingly advanced results in diverse problems, their lack of interpretability is a major problem, especially in fields such as genomics. We spoke to Avanti Shrikumar, a PhD student in Computer Science at Stanford University, to learn more.