Artificial Intelligence and Healthcare
Author: Health Sciences Library
Description: This is a topic guide for Artificial Intelligence in Health Care. An Information Specialist from Unity Health Toronto curates resources listed in the guide. Please use the tabs on the page to explore different resources, including the section related to health professions education.
Artificial Intelligence in Medical Education: Are We Ready For It?
Authors: Nazish Imran, Masood Jawaid
Type: Open Access Article
Description: “Our fate is to change.” Enrico Coiera emphasized in Lancet, while discussing fate of medicine in the time of Artificial intelligence (AI). The rapid growth of artificial intelligence in healthcare around the globe are glimmers of a future, where AI driven tools are likely to define the way medicine will be practiced in 21st century. Artificial Intelligence (AI) or the mimicking of human cognition by computers is conceptualized as a machine with intelligent behavior like reasoning, perception, ability to generalize and learn from experience. Literature suggests that AI systems and tools can help to deliver precision medicine, be faster, effective and as accurate as human clinicians and improve delivery of healthcare.
Big Data, the Science of Learning, Analytics, and Transformation of Education
Author: Candace Thille
Type: YouTube Video
Description: From the mediaX Conference “Platforms for Collaboration and Productivity”, Candace Thille, with the Stanford Graduate School of Education highlights the power of platform tools and technologies to transform observation and data collection. This process enables researchers from industry and academia to know their user better – as consumers, as producers, and as learners.
Connecting the Dots on Data for L&D
Authors: Michelle Ockers, Trish Uhl, Kevin M. Yates
Description: Digital transformation has been underway for quite some time. Data is part of the business ecosystem in which L&D operates. Where do the opportunities lie for L&D to use the stream of data to improve decisions and increase impact? Practical examples and future possibilities to help L&D professionals connect the dots on using data.
Educational Data Mining and Learning Analytics
Authors: Ryan S. Baker, Paul Salvador Inventado
Description: In this chapter, we discuss educational data mining and learning analytics (Baker & Siemens, 2014) as a set of emerging practices that may assist distance education instructors in gaining a rich understanding of their students
How to Read Articles That Use Machine Learning Users’ Guides to the Medical Literature
Authors: Yun Liu, Po-Hsuan Cameron Chen, Jonathan Krause, Lily Peng
Description: In recent years, many new clinical diagnostic tools have been developed using complicated machine learning methods. Irrespective of how a diagnostic tool is derived, it must be evaluated using a 3-step process of deriving, validating, and establishing the clinical effectiveness of the tool. Machine learning–based tools should also be assessed for the type of machine learning model used and its appropriateness for the input data type and data set size. Machine learning models also generally have additional prespecified settings called hyperparameters, which must be tuned on a data set independent of the validation set. On the validation set, the outcome against which the model is evaluated is termed the reference standard. The rigor of the reference standard must be assessed, such as against a universally accepted gold standard or expert grading.
Podcast 17: Next-Level Learning Analytics – The Talented Learning Show
Author: John Leh
Description: What should you expect from learning analytics tools? And how are innovative solutions taking learning measurement to a whole new level? Listen to The Talented Learning Show!
Practical Application of Learning Analytics & Data
Author: Sam Rogers
Description: The concept of Learning Analytics has been a hot topic for several years in the L&D industry. There are those that talk about it, and then there are those doing the work and making it happen within their organizations. So many people jump straight to purchasing one technology or another in an effort to get better analytics. But the information you get from the tech is only as good as the data being put into it and how well you understand the meaning of that data.
Trends in Data Analytics for Instructional Designers With Anna Leach
Authors: Brent Schlenker, Chris Van Wingerden, Anna Leach
Description: There are very few data analysts who understand instructional design, or care about it for that matter. As we all hear about the coming tsunami of data via xAPI and other technologies, we need to figure out how to use it. More importantly, we need to understand why it's important to gather data and plan how we will use it to improve the learning solutions we provide. We'll have a great conversation with Anna about her journey and what she's learned while blending the fields of Learning Technologies and Data Analytics.
Use of Learning Analytics Data in Health Care–Related Educational Disciplines: Systematic Review
Authors: Albert KM Chan, Michael G Botelho, Otto LT Lam
Type: Journal Article
Description: While the application of learning analytics in tertiary education has received increasing attention in recent years, a much smaller number have explored its use in health care-related educational studies. This systematic review aims to examine the use of e-learning analytics data in health care studies.
What Do Medical Students Actually Need to Know About Artificial Intelligence
Authors: Liam G. McCoy, Sujay Nagaraj, Felipe Morgado, Vinyas Harish, Sunit Das and Leo Anthony Celi
Description: With emerging innovations in artificial intelligence (AI) poised to substantially impact medical practice, interest in training current and future physicians about the technology is growing. Alongside comes the question of what, precisely, should medical students be taught. While competencies for the clinical usage of AI are broadly similar to those for any other novel technology, there are qualitative differences of critical importance to concerns regarding explainability, health equity, and data security. Drawing on experiences at the University of Toronto Faculty of Medicine and MIT Critical Data’s “datathons”, the authors advocate for a dualfocused
approach: combining robust data science-focused additions to baseline health research curricula and extracurricular programs to cultivate leadership in this space.