Abstract: Talk of artificial intelligence (AI) and machine learning (ML) has abounded in radiology circles for some years now. Their potential to revolutionize radiology practice, and in particular image analysis, is significant. And for some, the future of radiology has already arrived, reducing va
Abstract: In spite of the promise it holds and the anticipation it has generated, artificial intelligence (AI) in medicine is not immune to bottlenecks translating the technology from research to clinical practice. Medical professionals are often not actively engaged in developing these AI-based app
Date: January 30th: 9am PST - 10am PST Description This webinar will follow previous AIMed Talks format where we will discuss, in depth, the issues around Data Rights in Healthcare now that Machine Learning is becoming more prevalent. The following question will be discussed: How can we create a res
Date: February 18th 2020 Time: 8am - 9am PST / 11am - 12pm EST / 4pm-5pm GMT Description: Artificial Intelligence and Machine Learning (AI/ML) in the clinic is where the rubber hits the road. How are practitioners actually using AI/ML in their daily practice? What are the new challenges that provide
Date: March 31st 2020 Time: 1pm- 2pm ET /10am - 11am PDT / 6pm-7pm BST Description: The role of artificial intelligence in healthcare continues to evolve as does the definition of what it is and is not. This state of flux has contributed to slower than desired adoption, unmet expectations, and gap
Abstract The coronavirus pandemic has drastically accelerated the deployment of digital health solutions. Indeed, governments and health ministries across the world have been inundated with offers from tech firms wanting to do their bit in the fight against COVID-19. However, there is a risk that un
Abstract COVID-19 and its pandemic has disrupted our societies in a major way. Healthcare innovation and artificial intelligence are more needed than ever before as vital forces to meet the myriad of challenges of pandemics. This special seminar will gather some of the best minds in both innovation
Abstract The impact of data quality and quantity (on modeling) is always critical, no matter in traditional statistics or machine learning (deep learning). Data privacy is always the top consideration for digital health and health informatics. However, it becomes a significant obstacle when it comes
Abstract Data science holds the potential to increase the efficiency and effectiveness of intensive care units, as well as improving patient outcomes. A lot of research in this space focuses on predictive algorithms: solutions to support the early identification of shock, sepsis, hypotension, and ot
Abstract Data science holds the potential to increase the efficiency and effectiveness of surgical workflow, as well as improving patient outcomes in the operating room. Artificial intelligence is already informing risk prediction and decision support, increasingly drawing on live data from electron
