Spotlight: AI and Machine Learning
AI and machine learning are terms becoming increasing familiar across a range of industries and applications, as the world continues to evolve - with technological advancement at the core.
The COVID-19 pandemic has further accelerated the digital transformation across workplace systems, most notably in healthcare systems globally. Prior to the pandemic in-roads were already well underway with traditional health systems shifting to digital platforms. COVID-19 has intensified the need for research and the application of technologies as new approaches to healthcare delivery are sought moving forward. Machine learning can help automate contact tracing efforts during COVID-19 through analytical models that collate and reflect ‘real world data’, the Government’s COVIDSafe app collected data to be examined and analysed by AI to enable better management of exposure to the virus by the population, and, Professor’s in New York are also working to use clinical records of COVID-19 patients to create an AI-powered decision support tool to help clinicians quickly identify and predict patients most likely to experience severe symptoms.
AI and machine learning are the most trending technologies of today. While the two are correlated, they are in fact different: AI is a bigger concept to create intelligent machines that can simulate human thinking capability and behaviour, whereas, machine learning is an application or subset of AI that allows machines to learn from data without being programmed explicitly.
Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed by humans. Machine learning focuses on the development of computer programs that can access data and use it learn for themselves. The primary aim is to allow the computers learn automatically without human intervention or assistance and adjust actions accordingly .
Within the University of Adelaide’s Australian Institute for Machine Learning a number of exciting research activities and applications of machine learning are being applied in the healthcare setting and beyond. The articles contained within this edition of Thought Leadership (Insights) showcase how Dr Johan Verjans (Deputy Director, Medical Machine Learning – Australian Institute for Machine Learning & Consultant Cardiologist) is using machine learning to predict heart attacks, allowing for early prevention measures to be implemented, improving patient outcomes. Professor Gustavo Carneiro is undertaking research to devise computer-aided diagnosis in medical image analysis to predict breast cancer probability from mammograms. Lastly, Zygmunt Szpak (Senior Research Associate, Australian Institute of Machine Learning), in collaboration with a multi-disciplinary team, has explored ethical AI adoption in the workplace and how AI adoption might give rise to psychological risks for employees.
Craig McCallum, MBT, BBM (Hons)
Executive Director – Education Transformation