Course details
Tutors
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
Course content
In this course, you will build a practical understanding of how Artificial Intelligence (AI) is reshaping healthcare and how to make sense of the hype, the evidence, and the real-world constraints that determine whether AI improves care or creates new risks. You will start by grounding AI in the realities of healthcare delivery, comparing how health systems operate across different countries and why governance, standards, and decision pathways (including drug and technology approvals) shape what can be implemented safely and at scale.
You will then explore how digital transformation changes care delivery and operational efficiency, and what this means for the data that powers AI. You will learn why good models depend on well-designed data systems and representative datasets and you will use an “input–process–output” lens to evaluate data quality, model performance, and the clinical relevance of AI-generated insights. You will review core methods including machine learning, deep learning, natural language processing and emerging tools and examine case studies across diagnosis, treatment planning, and personalised medicine. Along the way, you will clarify the difference between predictive vs generative AI, and how both relate to (and differ from) traditional statistical modelling.
Responsible use and adoption of AI tools matters as much as technical capability. You will learn about current efforts addressing ethics and governance matters in health data space. You will look at privacy and security (including GDPR/HIPAA principles), bias and fairness, transparency and explainability, and the role of regulation and clinical validation in building trust. You will also look at integration challenges such as EHRs, telemedicine, and other digital tools and work through examples of successful transformation programmes.
By the end, you will contribute to co-development of a clear, actionable blueprint for a roadmap for operationalising AI in healthcare organisations, including where federation and wider data access can accelerate progress, and where practical barriers must be addressed first.
What to expect on this course
The course is taught in person, in an immersive classroom setting, designed to help you learn quickly, confidently, and alongside a cohort of peers who bring different perspectives from across healthcare and data-driven roles. Each session blends short, structured teaching segments with highly interactive activities so you can immediately apply ideas, test your understanding, and see how concepts translate into real-world decisions.
You can expect a mix of interactive presentations, live demonstrations, and guided discussion. We’ll use presentation tools to make the learning dynamic, think live polling and quick “checkpoint” questions, short scenario prompts, and group tasks that help you connect technical methods to practical healthcare challenges. To get the most from these activities, it’s strongly recommended that you bring your laptop (or equivalent device), as you will be able to join live voting, explore examples, and follow along with demonstrations.
A key strength of this in-person format is the energy and depth of learning you get from being in the room: you will be able to ask questions in the moment, compare approaches with peers, and learn through shared problem-solving. You will take part in small-group discussions and structured debates on topics such as AI evaluation, ethics, governance, and implementation barriers so you leave with both understanding and confidence in how to communicate these issues in your own workplace.
Throughout the course, you will work with case studies drawn from contemporary healthcare and research settings. These are used not as “nice-to-have” examples, but as a core teaching method helping you practise how to assess data readiness, interpret model outputs, and think critically about safety, bias, and clinical validation.
You will finish by contributing to a co-created implementation roadmap: a practical, peer-informed way to consolidate learning and translate it into next steps you can take after the course.
Course sessions
- 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. - 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. - 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. - 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. - 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
As a result of the course, you will gain a greater understanding of the subject and you should be able to:
- have 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
- 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
- assess the impact of digital transformation on patient outcomes and organisational efficiency
Required reading
There is no required reading for this course. See Course materials for supplementary reading once registered.