Join us in exploring the real-world applications of Artificial Intelligence
About this Event
The Science Innovation Union is proud to present the second in our Artificial Intelligence (AI) events series, exploring the use of AI in the HealthTech industry.
Experts and industry leaders will be sharing their personal journeys, their insights into the growing field, and offering their opinions on a range of topics in the HealthTech industry.
Expect to be fascinated, inspired, and educated on an innovative application of the disruptive technology that is AI.
Our free-for-all event will include a presentation by each of our guest speakers followed by a Q&A session which will be led by SIU hosts.
Please register via EventBrite to reserve a spot. All registered attendees will receive zoom joining details by email in advance of the webinar.
Peter Fish – Co-founder of Tuune
Peter is a medical doctor, scientist & entrepreneur. Dr Peter Fish holds degrees in molecular human genetics, medicine and an MBA. He is passionate about genomics, personalised medicine, innovation & big data in the medical world. Peter has spent the last 15 years building innovative companies and previously the Head of Clinical Strategy at Mendelian, a startup that builds digital systems to speed up the diagnosis of rare diseases and a consultant at the Wellcome Sanger Institute where he focused on a precision oncology project called COSMIC. He is a co-founder and the Chief Medical Officer at Tuune (previously Pexxi), a healthtech startup focused on female hormones and contraception.
Sarah Porter - Founder and CEO of InspiredMinds
Sarah is the Founder and CEO of InspiredMinds (IM) a global group active in 162 countries worldwide that specialises in Artificial Intelligence for good. In 2016, Sarah left her role as board director in a large corporate organisation to set up InspiredMinds. Her aim was to build the most powerful worldwide multidisciplinary community in science, technology, government and business, and use the power of AI and emerging technology to accelerate progress towards the United Nations 2030 Global Goals. IM now represents a worldwide community of 52,000 “gamechangers” including the support of the WHO, UN, EU parliament, NHS and many more.
As a community, IM has achieved a number of groundbreaking initiatives in the progress of AI4Good. IM projects include World Summit AI, Intelligent Health, ADA-ai and Clinect. Intelligent Health are a unique community of AI technologists, scientists and clinicians working to solve medical issues using AI, including heart disease, cancer diagnosis and treatment, Alzheimers and disease outbreaks and management in developing countries. Clinect is a global project to unite and matchmake clinical problems with AI tech solutions for the NHSx and WHO.
Josep Monserrat - Senior Scientist at BenevolentAI
Josep is a Senior Scientist working on data-driven target identification at BenevolentAI, a cutting-edge AI-driven drug discovery company. He works across a number of BenevolentAI’s internal drug programmes, leveraging BenevolentAI’s proprietary knowledge graph to contextualise relevant information for a given disease generated via relational inference AI models. He also designs, plans and oversees lab-based experiments for the validation of novel therapeutic targets.
Before joining BenevolentAI, Josep worked at Eli Lilly where he established new in vitro models to better understand neurodegenerative disorders; and obtained a doctorate in Cancer Biology at the Francis Crick Institute.
Kamar El-Kotob - Computer Vision Engineer at Third Eye Intelligence
Kamar El-Kotob is a Computer Vision Engineer at Third Eye Intelligence. She joined the team in December of last year. Kamar holds a MSc in Human and Biological Robotics from Imperial College London and a BEng in Electrical and Computer Engineering from the American University of Beirut. Her work at Third Eye Intelligence focuses on the generation of synthetic medical images utilizing state-of-the-art methods in Generative Modelling.
At Third Eye Intelligence, the aim is to utilise AI on routinely collected clinical data to predict risk of patient deterioration, mortality and length of stay, in view of guiding doctors in evidence-based decision making. Given the scarcity of the training data, the Computer Vision team is currently working on the development of a synthetic medical image generator network for automated image augmentation.