Delivery format
In person
Course overview
Join our Foundations of AI Technologies in Healthcare CPD course to understand the breadth of AI technologies and their growing potential in healthcare. This introductory course features guest lecturers from industry, NHS, and academia, offering diverse insights accessible to applicants from clinical, operational or technical backgrounds.
Over five days, engage in seminars, practical exercises, panels, and fireside chats with leaders in clinical AI. No coding expertise is required, though attendees will develop practical insights through a code-free approach to model development. Equip yourself to identify the opportunities and challenges AI presents in different healthcare contexts and enhance your professional skills in this dynamic field.
Course delivery
Day 1: Introduction to AI frameworks
Begin with an overview of AI's historical development, from rule-based systems to neural networks. Then with case studies, take a deeper dive into the different AI frameworks including traditional machine learning (ML), deep learning (DL) and generative AI (GAI) - understanding how they learn from data. Learn principles to to select appropriate AI frameworks for a given task.
Day 2: Data Management
Focus on information governance and data protection legislation and develop data flow diagrams to better understand and communicate how data is used in example AI systems. Discuss key issues in the use of healthcare data like bias, accuracy and system interoperability. Critically appraise datasheets and learn how data is prepared for model development, including practical exercises using no-code tools.
Day 3: Generative AI in Healthcare
Explore the evolution, architecture, and capabilities of generative AI systems. Review case studies of current applications in healthcare, such as synthetic data for model evaluation and ambient voice technologies. Learn best practices for instructing generative systems including strategies for hallucination detection and mitigation.
Day 4: AI Evaluation and Explainability
Understand key evaluation measures for generative AI and other AI frameworks across a range of tasks. Learn to interpret these measures of performance and estimates of their reliability. Explore AI explainability and transparency through case studies and the critical appraisal of model cards.
Day 5: Practical Skills and Future Trends
Apply a code-free approach to develop your own AI model from datasets. Engage in practical exercises and technical assurance as an adopter. Discuss the environmental impact of AI with an exemplar case study from the evaluation of a real-world AI deployment. Conclude with a horizon scanning talk, considering potential future developments in AI and their implications for healthcare.
On completion of the course attendees will be able to:
- Evaluate the benefits and risks of AI subtypes for use in specified tasks
- Analyse the provenance and use of data in developing and validating AI technologies to critically appraise the impact that data characteristics have on the apparent performance of a specific AI technology
- Analyse common AI explainability outputs to critically appraise outputs from AI models
- Evaluate AI use cases to select appropriate performance statistics and apply them to evaluate AI models
- Create and evaluate an AI model for a specific use case
Dates of the course
17 November 2025 - 21 November 2025
Guest lecturers
Stephen Perks, Principal Technical Lead, West Midlands Secure Data Environment, University Hospitals Birmingham NHS Foundation Trust
Dr Anmol Arora, NIHR Academic Clinical Fellow in Medical Oncology, University College London
Dr Tafsir Ahmed, National Medical Director's Clinical Fellow to Care Quality Commission, Primary & Community Care
Dr Justin Engelmann, Research Fellow, University College London
Professor Bilal Mateen, Chief AI Officer, PATH
Adam Byfield, AI Quality Community of Practice Lead, NHS England
Dr Aisling Higham, Medical Director, Ufonia
Dr Vaishnavi Menon, AI and Digital Health Clinical Research Fellow, University Hospitals Birmingham NHS Foundation Trust
Maddy Griffiths, Senior Policy Officer, Innovation Hub, Information Commissioner’s Office
Professor Pearse Keane, Professor of Artificial Medical Intelligence, University College London
Professor Peter Bannister, Managing Director, Romilly Life Sciences
Hannah Richardson, Senior Manager, Compliance & Regulatory, Microsoft Research – Health Futures
Teaching staff
Staff involved in the delivery of this course
Professor Slava Jankin
Chair in Data Science and Government
Professor of data science and government in the School of Government, College of Social Sciences and the School of Computer Science, College of Engineering and Physical Sciences.
Dr Jeffry Hogg
Honorary Clinical Research Associate
Staff profile for Dr Jeffry Hogg, Honorary Clinical Research Associate, College of Medicine and Health, University of Birmingham.
Dr Joie Ensor
Associate Professor in Biostatistics
Staff profile page for Dr Joie Ensor, Associate Professor in Biostatics within the Department of Applied Health Sciences, College of Medicine and Health, University of Birmingham.
Professor Alastair Denniston
Chair of Regulatory Science and Innovation
Alastair Denniston is Professor of Regulatory Science and Innovation at the University of Birmingham. He is a leader in the field of Artificial Intelligence (AI) and Digital Health Technologies.
Dr Joseph E. Alderman
NIHR Clinical Lecturer in Anaesthetics
Dr Joseph Alderman, AI and digital health clinical research fellow based within the Department of Inflammation and Ageing, University of Birmingham.
Dr Xiaoxuan Liu
Honorary Associate Professor in AI and Digital Health Technologies
Dr Xiao Liu is an Honorary Associate Professor in AI and Digital Health at the University of Birmingham.
Entry requirements
This course is suitable for recent graduates from healthcare, management, technical or innovation degree programmes. It is also targets mid-career applicants with practical, policy or research roles in healthcare or medtech.
Fees and scholarships
£1011.11
Application process
Register via the online shop.
Last day to book is Monday 3 November.
If you have any queries, please email cmhcpdenquiries@contacts.bham.ac.uk.
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