AI for Healthcare: Equipping the Workforce for Digital Transformation

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Free Online Course: AI for Healthcare: Equipping the Workforce for Digital Transformation provided by FutureLearn is a comprehensive online course, which lasts for 5 weeks long, 2 hours a week. The course is taught in English and is free of charge. Upon completion of the course, you can receive an e-certificate from FutureLearn. AI for Healthcare: Equipping the Workforce for Digital Transformation is taught by Andy Brass.

Overview
  • Build your digital understanding and become a champion for AI in healthcare

    AI is transforming healthcare in a variety of beneficial ways, from streamlining workflow processes to making more precise patient diagnoses. However, this is not without its challenges.

    The University of Manchester has partnered with Health Education England to create a course for you to see real-world examples of how AI is transforming areas such as radiology, pathology, and nursing.

    On this course, you will develop your own digital skills and increase your understanding of technology for healthcare, so that you can join the conversation on embedding AI in healthcare practice.

    This course is designed for health and social care professionals in the UK.

    It is relevant to any non-UK healthcare professionals who want to understand the basic concepts, challenges and opportunities of AI in their respective healthcare systems.

    The course is also useful for those with relevant skills outside the NHS who have an interest in new professional groups emerging from AI, such as clinical data scientists, medical software engineers, and digital medicine specialists.

Syllabus
    • Motivating AI in healthcare
      • Getting Started
      • Joining the Conversation
      • The Fourth Industrial Revolution
    • What is artificial intelligence?
      • AI and machine learning
      • Machine learning workflow
      • Data in the machine learning workflow
    • Data in healthcare
      • Challenges
      • How data is being used
      • Towards the future
    • Making it work
      • Ethics and consent
      • Working in interdisciplinary teams
      • New ways of working
    • Supporting and skilling the workforce
      • Using machine learning for cancer diagnosis
      • Translation into practice
      • Looking ahead