Aims
This course aims to:
• introduce you to the fundamental concepts of Artificial Intelligence and the digital transformation in healthcare
• provide you with practical examples of operational scenarios
• equip you with tools, techniques and terminology involved in evaluation of robustness of an
AI-based decision tool in healthcare setting
Content
In this course, we will cover fundamental concepts of Artificial Intelligence and the digital transformation in healthcare. We will develop an understanding of healthcare systems exploring the variety in approaches in the sector, across the globe. Procedures for drug approvals and, standardisation of practices and methods. We will review the principle of data systems in healthcare and pave our way to areas in which AI approaches are actively being used. The course then moves to the understanding of what the AI revolution in healthcare means? We will approach this with a focus on data requirements, computational requirements for advanced analytical algorithms such as deep learning and the ethical deployment of AI models and insights. We will also discuss differences between generative and predictive AI models between traditional statistics. We will present the concepts of designing a medical study with a machine learning focus, and how technological advancements contribute to AI-focused initiatives.
Prior to studying the advanced concepts, we will discuss an overview of AI and its application in healthcare. We will review the digital transformation framework and how it applies to healthcare systems and evaluate the effects of digital transformation in care delivery and operational efficiency.
AI Models require a well-designed data model that is developed on a well-representative data set. In the second session we will focus on quality of data (input), the AI models (processor) and the quality and relevance of the outputs of AI Models. We will look at the current AI methods such as Machine Learning, Natural Language Processing, Image analysis and Robotics. We will illustrate case studies in successful applications of AI in diagnosis, treatment and personalised medicine. We will also review the work of this year’s Nobel Laureates in physiology and medicine awards won by Katalin Karikó and Drew Weissman and discuss various aspects of vaccine development that have been influenced by digital transformation, and how this can contribute to future challenges.
Next, we discuss the very hot topic of ethics and governance. In the context of adopting AI methods in healthcare, ethics and governance are pivotal considerations. Ensuring data privacy and security is paramount, adhering to regulations like GDPR and HIPAA to safeguard patient information. Transparency and explainability of AI decisions are essential for fostering trust among clinicians and patients. We will be looking at current practices involved in regulatory standards and rigorous clinical validation which are necessary in leveraging AI's potential for enhancing healthcare while upholding patient welfare and trust.
In the penultimate session, we will discuss integration of electronic health records (EHRs), telemedicine, and other digital tools and the challenges involved therein. We will also consider case-studies on successful digital transformation initiatives in healthcare and health research.
A group-led discussion will cover development and adoption of AI-based solutions in healthcare organisations.
The focus of the final session will be on the creation of a road map for AI development in healthcare by critical evaluation of possibilities for operationalised approaches in adoption of AI. You will be engaged in a joint development of this roadmap through a classroom discussion with the peer-learners. We will focus on the importance of federation and global access to health and the role of AI in this; consider practical scenarios where digital transformation is already bringing advancement in clinical practice such as robotic arms in surgical rooms, artificial intelligence, telehealth and blockchain. We will conclude with a summary of the course.
Presentation of the course
The course will take place in a classroom setting using interactive presentation tools to aid with demonstrations of technical methods. It is highly recommended that you bring your personal laptop or IT equipment to be able to follow some of the live voting and technical experiences with tools. Students will be encouraged to contribute to discussions in the classroom by offering opinions, experiences and observations.
Course sessions
1. Introduction to AI and digital transformation
An overview of AI and its application in healthcare. The digital transformation framework and how it applies to healthcare systems. The effects of digital transformation in care delivery and operational efficiency.
2. Digital transformation
Quality of data (input), the AI models (processor) and the quality and relevance of the outputs of AI Models. Machine Learning, Natural Language Processing, Image analysis and Robotics. Successful applications of AI in diagnosis, treatment and personalised medicine. Work of this year’s Nobel Laureates in physiology and medicine awards won by Katalin Karikó and Drew Weissman. Vaccine development influenced by digital transformation.
3. Governance and ethics
Ethics and governance. The Track and Trace (TT) system for pandemic control and differences in global practices on data retention and usage. Data privacy, security and interoperability; regulatory frameworks and compliance standards.
4. AI driven healthcare
Integration of electronic health records (EHRs), telemedicine, other digital tools and challenges. Case-studies on successful digital transformation initiatives in healthcare and health research. Discussion of development and adoption of AI-based solutions in healthcare organisations.
5. Navigating the Future: Crafting an AI Roadmap for Healthcare
Creating a road map for AI development in healthcare. Global access to health and the role of AI in this. Examples of digital transformation in clinical practice such as robotic arms in surgical rooms, artificial intelligence, telehealth and blockchain. Course summary.
Learning outcomes
You are expected to gain from this series of classroom sessions a greater understanding of the subject and of the core issues and arguments central to the course.
The learning outcomes for this course are:
• a principle understanding of the factors involved in leveraging AI Technologies in healthcare, including an understanding of various AI technologies and the ethical challenges associated with adoption of AI in healthcare and its potential impact on patient care
• the ability to identify and articulate a digital transformation strategy tailored to healthcare organisations. This involves identifying key areas for integration, such as local organisational capacities to large scale electronic health records, telemedicine, and data analytics, and understanding the practical steps and considerations in implementing these strategies
• an ability to assess the impact of digital transformation on patient outcomes and organisational efficiency
