Deep Learning in Healthcare Summit schedule

Lydia is a digital anthropologist who researches, writes and communicates about issues where data, culture and bodies meet. Formerly a senior researcher in Nesta's Futures Team and Health Lab she is now pursuing a PhD with UCL's Interaction Centre and Great Ormond Street Hospital. She has worked with the UK Government, the Science Museum, the Wellcome Trust, and more, appeared on BBC Worldwide and Radio 4, writes about science and culture for the New Scientist and regularly performs stand-up comedy about futures & science.

Let The Computer Read

HubScience is an scientific software development and service provider company founded in 2015. A team with over 12 years of experience in the development of commercial databases and innovative decision making solutions with primary focus on the pharmaceutical and biotechnology industry ensures the high quality technology. HubScience offers versatile technology to accelerate the knowledge extraction of scientific literature in order for researchers to be able to utilize the existing scientific achievements, and to generate machine readable facts for further analytics. Users can train a built-in AI to recognize pre-defined information categories to automatically analyse large text within a given topic. By supporting cooperation AI-training process and knowledge-share is facilitated.

Timea received her Ph.D. in pharmaceutical sciences with emphasis in chemical engineering and computational approaches for early phase drug discovery at the Budapest University of Technology and Economics, graduating summa cum laude. Her postdoctoral training was in biochemistry, genetics and molecular biology at Albert Szent–Györgyi Medical University in Hungary. She received a M.Sc. in chemistry with emphasis in computational approaches and a M.Sc. in molecular biology. Her broad experience includes strategic business development and research and development with over 18 years in pharmaceutical research and development, biotechnology and scientific software development. She has all–encompassing technical and scientific expertise in drug discovery, with specialty in network pharmacology, ligand–based and structure–based drug design, virtual screening, chemical and biopharmaceutical database management, high–content data analysis and genetics. She has published in about 30 peer reviewed journals and book chapters in the scientific literature.

Towards automated clinical coding

Manual clinical coding is expensive, unstandardised and incompatible with real-time data interpretation. Traditional automated systems fail to capture much of the information contained in clinical notes. Automated clinical coding is challenging due the large number of clinical codes versus the amount of training data available, and the rarity of many important diseases. Our system uses deep learning to produce rich representations of clinical notes, allowing more accurate coding. We use the structure of medical knowledge to learn more efficiently from training data and allow better coding of rare diseases.

Finn Catling is a doctor, machine learning researcher and entrepreneur. He is the founder of Decode Healthcare, a startup which uses AI to drive new insights, better outcomes and improved efficiency for hospitals and GP practices. His recent research focuses on automated clinical coding, prediction of ventilator-associated pneumonia, prediction of demand on emergency services and generation of synthetic radiotherapy data. Previously, he co-founded the medical education startup T-Log and the Data Science for Doctors courses.

Predictive Algorithms and their Impact in Chronic Diseases Management: Diabetes Case

Eduardo will share his vision on the major role that artificial intelligence can have in the digital transformation of the healthcare sector. As a medical doctor and CEO of a medical devices company, he has negotiated with major stakehoders and has clear insights about the needs of both the industry and the patient. Personalized self care, preventive diagnosis, individual treatments and patient support programs, everything available thanks to predictive models and easy interfaces.

Eduardo W. Jorgensen: 2015 UAM Bachelor in Medicine, Built several teams for medical research doing college, passionate about medicine and new technologies, specially in the area of neuroscience and perception. My goal is to change the wy we live with chronic diseases. Spanish national with great interest in medicine, neuroscience and technologies. Perfect domain of English for fluent conversations and negotiations. Had an enrollment of honor prior to college, got into medical school in 2009 and graduated in 2015. In the period, got selected for StartupsMansion (NYC entrepreneurs program), had funded two companies: Livinplans (Surprise travelling agency) and MedicSen (non invasive artificial Pancreas for diabetes) and have been accelerated by TURN8 in Dubai. At the end of the day, I try to learn as much as possible about the world and get surrounded by the top innovative people to form good multidisciplinary teams capable of disrupting every aspect.

Deep Learning in Medical Imaging - Successes and Challenges

Machines capable of analysing and interpreting medical scans with super-human performance are within reach. Deep learning, in particular, has emerged as a promising tool in our work on automatically detecting brain damage. But getting from the lab into clinical practice comes with great challenges. How do we know when the machine gets it wrong? Can we predict failure, and can we make the machine robust to changes in the clinical data? We will discuss some of our most recent work that aims to address these critical issues and demonstrate our latest results on deep learning for analysing medical scans.

Ben Glocker is Senior Lecturer in Medical Image Computing at the Department of Computing at Imperial College London, and one of three academics leading the Biomedical Image Analysis Group. He also leads the HeartFlow-Imperial Research Team and is scientific advisor for London-based start-up Kheiron Medical Technologies. His research is at the intersection of medical image analysis and artificial intelligence aiming to build computational tools for improving diagnosis, therapy and intervention. He has received several awards including a Philips Impact Award and the Francois Erbsmann Prize. He is a member of the Young Scientists Community of the World Economic Forum. His ERC Starting Grant MIRA is devoted to developing the next generation machine intelligence for medical image representation and analysis.

Do Transformers See Like Convolutional Neural Networks?

Deep learning capabilities have arguably entered a new stage since around 2020. These new capabilities also rely on new design principles, with self-supervision, large scale pretraining and transfer learning all playing central roles. Perhaps most strikingly, this new stage of deep learning makes critical use of Transformers. Initially developed for machine translation tasks, then rapidly adopted across different NLP tasks, Transformers have recently shown superior performance to Convolutional Neural Networks (CNNs) on a variety of computer vision tasks. As CNNs have been the de-facto model for visual data for almost a decade, this gives rise to a fundamental question: how are Transformers solving visual tasks, and what can we learn about their successful application? In this talk, I present results providing answers to these questions. I overview key differences in image representations in Transformers and CNNs, and reveal how these arise from the differing functions of self-attention vs convolutions. I highlight connections between pretraining and learned representations, and explore ramifications for transfer learning and the role of scale in performance.

Maithra Raghu is a Senior Research Scientist at Google Brain and finished her PhD in Computer Science at Cornell University. Her research broadly focuses on enabling effective collaboration between humans and AI, from design to deployment. Specifically, her work develops algorithms to gain insights into deep neural network representations and uses these insights to inform the design of AI systems and their interaction with human experts at deployment. Her work has been featured in many press outlets including The Washington Post, WIRED and Quanta Magazine. She has been named one of the Forbes 30 Under 30 in Science, a 2020 STAT Wunderkind, and a Rising Star in EECS.

Applications of Deep Learning to Neurotherapeutics Development

In this talk, we will outline some of the major obstacles to therapeutics development for neurological and psychiatric diseases and how deep learning might be used to address them. We developed robotic microscopes and patient stem cell models of disease that we use to generate large training sets for deep learning networks to develop algorithms that could help with patient stratification, model development, diagnosis, target identification and drug discovery. In one particular example, we collaborated with engineers from Google and recently developed a new technology called in silico tagging in which deep learning networks were trained to accurately predict cell structures, cell states and cell types from unlabeled images obviating the need to perform labeling. This technology will enable investigators to glean much more information from their data than previously possible for almost no additional cost. We see tremendous opportunities for applications of deep learning to contribute to the development of treatments for some of the most devastating diseases known.

Dr. Finkbeiner trained at Yale University, UCSF and Harvard University before joining the faculty at the Gladstone Institutes and UCSF in 1999. Since then, he has been promoted to his current position as a Director at the Gladstone Institutes and a Professor of Neurology and Physiology at UCSF. He Directs the Center for Systems and Therapeutics and the Taube/Koret Center for Neurodegenerative Disease. His research has focused on basic science and disease-related questions in neuroscience, particularly fundamental questions related to learning and memory and elucidating mechanisms of neurodegenerative disease and mental illness. In 2009, the Taube/Koret Center was established to find catalyze the development of neurotherapeutics, leveraging discoveries and technology from in the academic laboratory. Early on, Dr. Finkbeiner developed robotic microscopy, a high throughput longitudinal single cell imaging and analysis approach. It provides a way for scientists to quantify the prognostic value of cellular and molecular changes during a cell’s lifetime for some important future event. It helps to overcome limitations in sensitivity and observer bias inherent to conventional approaches based on single snap shots in time, and it has proven to be very valuable for developing a systems understanding of biology and pathobiology, developing disease models particularly based on induced pluripotent stem cells and for finding putative therapeutics. In turn, this technology has been useful for generating data at a scope and scale that is ideally suited for deep learning networks to develop powerful predictive algorithms and to make important unbiased discoveries in large complex datasets. His laboratory has applied the approach in studies of Parkinson’s disease, Huntington’s disease, ALS, Alzheimer’s disease, frontotemporal dementia, autism and schizophrenia.

Jade is a researcher with the Governance of Artificial Intelligence Program (GovAI) at the Future of Humanity Institute (University of Oxford). Her research focuses on the governance of emerging dual-use technologies, with a specific focus on firm-government relations in the US and China with respect to advanced artificial intelligence. Jade has a background in engineering, international law, and policy design and evaluation.

Alison leads the Humanities work at the PHG Foundation, a health policy think tank which is part of University of Cambridge. Her research focuses on the regulation and governance of genomic data for clinical care and research, the impact of automated processing and artificial intelligence on existing legal and ethical frameworks, and the challenges and opportunities associated with delivering personalised healthcare. Alison has professional qualifications in law and nursing and a masters qualification in healthcare ethics.

Andrea Renda is an Italian social scientist, whose research lies at the crossroads between economics, law, technology and public policy. He is Senior Research Fellow and Head of Global Governance, Regulation, Innovation and the Digital Economy at the Centre for European Policy Studies (CEPS). From September 2017, he holds the Chair for Digital Innovation at the College of Europe in Bruges (Belgium) where he has also leading the course “Regulatory Impact Assessment for Business” since 2007. He is also a non-resident fellow at Duke University's Kenan Institute for Ethics. Over the past two decades, he has provided academic advice to several institutions, including the European Commission, the European Parliament, the OECD, the World Bank and several national governments around the world. An expert in technology policy and better regulation, he is a member of the ESIR (Economic and Social Impacts of Research) expert group of the European Commission; and a member of the EU Blockchain Observatory and Forum. He is also a member of the Editorial Board of the international peer-reviewed journals “Telecommunication Policy” (Elsevier) and of the European Journal of Risk Regulation (Lexxion); a member of the Scientific Board of the International Telecommunications Society (ITS) and Chair of the Scientific Board of European Communications Policy Research (EuroCPR). He holds a Ph.D. degree in Law and Economics awarded by the Erasmus University of Rotterdam.

Dr Loubna Bouarfa is a machine learning scientist turned entrepreneur. She is the founder and CEO of OKRA Technologies - an artificial intelligence data analytics company for healthcare. OKRA allows healthcare professionals to combine all their data in one place and generate actionable, evidence-based insights in real time, to save and improve human lives. Loubna is currently a member of the European Union High-Level Expert Group on Artificial Intelligence, where she is particularly focused on healthcare and achieving competitive business impact with AI. She was named an MIT Technology Review Top Innovator Under 35, a Forbes 50 Top Women In Tech, and won several prizes, including CEO of Year 2019 at the Cambridge Independent Science and Technology Awards and Best Female-Led Startup at the StartUp Europe Awards. On a personal level, she is a strong advocate for diversity, women and challenging the status quo.

Dr Matthew Fenech is an artificial intelligence policy consultant, with expertise in developing and advocating for policies that maximise the opportunities and minimise the risks of these technologies. His main interest is in the ethics and practicalities of the use of AI in healthcare, a field to which he brings his 10 years of experience working as a hospital doctor and clinical academic. He has also authored reports about AI and other emerging technologies in low- & middle-income countries, and on the impact of automation on the future of work. He regularly speaks about these topics in lectures and in the media.

Alice reviews Hazy's product features for AI ethics, data privacy and compliance and helps define Hazy's core message to the world. Prior to joining Hazy Alice coordinated the cross-party parliamentary group on AI (APPG AI), helping the UK Government to address ethical implications and design new standards for applying machine learning in commercial, political and social areas.

Alice has over 10 years of experience in policy, research, product management and marketing, and a particular focus on such fields as artificial intelligence, big data and tech for good. Having worked in national and international public and private sector organisations, social enterprises and NGOs, Alice has a proven track record in delivering the strategic vision and showcasing impact to a wide range of stakeholders.

Aimee van Wynsberghe has been working in ICT and robotics since 2004. She began her career as part of a research team working with surgical robots in Canada at CSTAR (Canadian Surgical Technologies and Advance Robotics). She is Assistant Professor in Ethics and Technology at TU Delft in the Netherlands. She is co-founder and co-director of the Foundation for Responsible Robotics, on the board of the Institute for Accountability in a Digital Age, and an advisory board member for the AI & Intelligent Automation Network. Aimee also serves as a member of the European Commission's High-Level Expert Group on AI and is a founding board member of the Netherlands AI Alliance. Aimee has been named one of the Netherlands top 400 influential women under 38 by VIVA and was named one of the 25 ‘women in robotics you need to know about’. She is author of the book Healthcare Robots: Ethics, Design, and Implementation and has been awarded an NWO personal research grant to study how we can responsibly design service robots. She has been interviewed by BBC, Quartz, Financial Times, and other International news media on the topic of ethics and robots, and is often invited to speak at International conferences and summits.

Caryn is an Analytics Strategist operating at the intersection of applied analytics and law/ethics.

She advises senior decision-makers on analytics strategy, target operating model and analytics business case and manages technical teams to operationalise and realise these strategies. She also manages Accenture’s Responsible AI practice in the UK where she helps clients confidently deploy responsible AI models with technical, organisational, governance and brand considerations. This involves working with multidisciplinary teams, industry experts and academic institutes.

Caryn graduated from London Business School and holds a law degree from BPP University, both as a merit scholar.

This is a 2-hour workshop with a maximum of 20 places, and registration is required. To register your interest, please complete the registration form here. You must already be registered for the summit in order to attend. An email will be sent to confirm your place.

When searching for “professor” or “CEO” on Google images, the results show overwhelmingly white male pictures. While these jobs are held by white male professionals more often, the image search results present an extreme bias against representing women and people of color. This has been pointed out as an ethical problem in various outlets; however the problem persists.

In this workshop, we use this case as an example on how to structure the ethical problem at hand and its underlying principles before moving on to try solving it. Through the game-like structure of the Mapping method, the workshop will engage participants and help them develop essential tools to decide on ethical solutions that are technically feasible. Collaborating with each other, participants test the strength of their ideas and progress gradually towards creating solutions to this real-life problem as well as analyzing how their solution would hold up in other relevant cases such as voice assistant responses and other search result categories. The Mapping helps bring abstract ethical arguments to the ground—in a very literal sense, since the Mapping takes the form of a physical ground game.

Limited spaces are available for this session. Sit down with experts in AI who are using deep learning and AI techniques to solve challenges in the healthcare sector. Offer them your thoughts on the problems they're facing in their work, get feedback on your challenges, and ask those burning questions in this in-depth networking session.

Confirmed experts include:

• Danielle Belgrave, Machine Learning Researcher, Microsoft Research

• Sarah Culkin, Strategic Data Lead, NHS England

• Daniel Leightley, Post-Doctoral Research Associate, King's Centre for Military Health Research

Questions and challenges our experts are keen to explore with you include: - How do we incorporate expert knowledge into Deep Learning? - Who is responsible when the ‘machine’ makes an incorrect decision that costs lives? - How do we build and develop trust in AI technology, especially within the healthcare sector? - Machine learning for Healthcare in Data Sparse Contexts - What advanced technologies do you think will emerge for the Health and Care system over the next decade?

Graphs are a natural way to model many real-world complex objects. In this talk, after a brief review of recent advances in using deep learning for graphs, we will present our approach to create inference models based on novel attention mechanisms for graph convolutional neural networks making them robust to noise with added interpretability. We will show their applications to very large semi-automatically generated biological networks. In addition, we will discuss our recent approaches to model and generate graphs with optimal properties using RL as well as an architecture for conditional generative graph models which are applied to create novel chemical compounds.

How to Design AI with Human Centricity

Whether we like it or not, we are inevitably moving into a world of more autonomous decisions by AI. In fact, done right it could lead to a really exciting future.

Done right is the key.

How do we ensure that we build AI that are not marginalising groups within society?

How do we prevent negative unintended consequences such as creating larger social disparity?

This workshop will get execs and data scientists thinking about how to ensure their AI is designed with human centricity in mind. Opened to everyone, both technical and non technical.

Parker Moss, Entrepreneur-in-Residence at F-Prime Capital will be chairing this exciting session exploring investing in startups. We will start off the session with a Founder's Story interview with Eduardo Jorgensen, Founder of MedicSen; a startup dedicated to healthcare who have seen significant growth over the past year since launching GlycSen for intelligent treatment of insulin dependent diabetes. Following this, leading investors interested in deep learning across all industries will be sharing industry insights and tips on a panel discussion.

The investors are:

Dmitry Kaminskiy, Managing Partner, Deep Knowledge Ventures

John Spindler, General Partner, AI Seed

Frederic Lardieg, Partner, Octopus Ventures

Attendees will be invited to meet the investors for the remainder of the session to ask those burning questions.