Practical training on deploying AI into healthcare systems
Artificial intelligence (AI) is poised to revolutionise healthcare, offering clinicians new tools to help diagnose, manage, and treat patients more efficiently and effectively.
This micro-credential is ideal for healthcare professionals aiming to excel in the evolving medical technology (medtech) landscape, exploring the role and importance of AI and machine learning in modern healthcare settings.
In this online course, you’ll gain a valuable understanding of AI’s potential in healthcare, while being introduced to best-practice approaches for implementing AI solutions that strengthen patient outcomes.
Delivered by leading medical technology experts, this course is endorsed by the Victorian Medtech Skills and Devices Hub, a respected provider of industry-aligned and future-ready medtech education courses.
Commences April 8, 2024
Explore AI fundamentals in healthcare
Gain a comprehensive understanding of AI and machine learning’s role and significance in modern healthcare, exploring their transformative potential as well as associated data privacy and regulatory considerations.
Understand machine learning’s healthcare impact
Learn the key differences between machine learning and deep learning and explore their specific applications and ethical implications in healthcare settings. Collaborate with your peers to recognise the benefits, challenges, and limitations unique to using AI and machine learning technologies in patient treatment scenarios.
Develop strategic AI deployment approaches
Acquire the strategic planning, stakeholder management, and ethical decision-making skills necessary to effectively deploy AI technologies into healthcare systems. Learn the essential data analysis and risk management skills needed to assess AI deployments in a healthcare setting and achieve project success.
Design AI healthcare solutions
Craft and propose an AI solution for a healthcare setting of your choice, focusing on its objectives, anticipated outcomes, challenges, and risks, followed by peer and expert evaluation.
