Is Artificial Intelligence (AI) a Solution for Healthcare? | Online

About this event

We're in a time when Artificial Intelligence (AI) is being debated in terms of how much it could cost humans. Join us for an event with Stratified Medical to discuss how AI can make human lives better. We'll look at AI in terms of healthcare, most notably addressing how it can help treat hundreds of diseases.

Talk 1: Deep Learning & AI - The Future of Healthcare?

Deep Learning and other machine learning/AI approaches are widely seen as a truly transformative technology with huge potential to benefit society and health. The pharmaceutical business sector is clearly at a transitional stage, with substantial restructuring underway to address systemwide failures to innovate and discover new therapies - in spite of having the component list and some elements of the fundamental interconnections and regulation of disease from the human genome project. We are developing a very large-scale system for data integration, life-science knowledge representation, and associated decision support tools with the aim of discovering new effective drugs at high efficiency and lower failure. The talk will outline our approach, detail why we think AI will be transformative in this sector, and show some specific examples from our research.

Talk 2: Automated Fact Checking for Healthcare

In recent years, the amount of information available online has increased rapidly, including healthcare related topics. Thus often patients address their questions first to a search engine to get answers from forums, social networks, etc. Unsurprisingly, while this can be helpful in obtaining help faster and without burdening healthcare professionals directly, it also has dangers since a lot of the easily accessible information can be mistaken and/or incomplete. As it would be impossible for experts to check all possible sources of (mis-)information, it would be helpful to develop automated methods to fact-check them. This talk will outline our vision and present some early work, drawing on our experiences from interacting with journalists who face similar challenges.