Module manager: Andrew Davies
Email: a.g.davies@leeds.ac.uk
Taught: Semester 2 (Jan to Jun) View Timetable
Year running 2026/27
This module is not approved as an Elective
Artificial Intelligence (AI) is rapidly transforming the landscape of healthcare, from diagnostic imaging and clinical decision-making to patient monitoring and administrative workflows. This module offers a critical and interdisciplinary exploration of AI technologies in healthcare, designed for postgraduate students from diverse backgrounds including radiography, medicine, and physical sciences. Through a combination of lectures, seminars, case studies, and hands-on activities, students will examine how AI is being used to enhance clinical practice, improve patient outcomes, and streamline healthcare systems. The module will introduce key concepts in AI such as machine learning, neural networks, and natural language processing, with a focus on their real-world applications in healthcare settings. Importantly, the module goes beyond technical understanding to address the ethical, legal, and societal implications of AI in healthcare. Students will explore issues such as algorithmic bias, data privacy, transparency, and the impact of automation on professional roles. These discussions will be grounded in current research and policy frameworks, encouraging students to think critically about the responsible development and deployment of AI technologies. Students will develop the ability to evaluate AI tools and systems from both clinical and technical viewpoints. They will also gain skills in interpreting AI-generated outputs, assessing data quality, and communicating complex ideas to varied audiences. The module encourages collaborative learning and reflection, preparing students to contribute thoughtfully to the evolving field of AI in healthcare. Whether you are a healthcare professional seeking to understand emerging technologies, a scientist interested in translational applications, or a clinician aiming to shape the future of patient care, this module provides a valuable foundation. It is particularly relevant for those looking to work at the intersection of technology and healthcare, and for anyone interested in the ethical and societal dimensions of innovation. By the end of the module, students will be equipped to critically assess the role of AI in healthcare, advocate for ethical practices, and engage with future developments in this dynamic and impactful field.
This module aims to equip students with a critical understanding of how artificial intelligence (AI) is reshaping healthcare practice and research. It explores the opportunities and challenges presented by AI in clinical environments.
The primary objective is to develop students’ ability to evaluate AI technologies from both technical and ethical perspectives. Students will gain insight into the principles of AI, including machine learning, data-driven decision-making, and automation, and how these are applied in healthcare settings such as diagnostic imaging, patient triage, and predictive analytics.
Equally important is the module’s focus on the ethical, legal, and societal dimensions of AI. Students will critically examine issues such as data governance, algorithmic bias, patient consent, and the implications of AI for professional roles and responsibilities. This will enable students to engage thoughtfully with debates around fairness, accountability, and transparency in healthcare innovation.
Learning activities are designed to support these objectives through a blend of theoretical and applied approaches. Lectures and seminars will introduce key concepts and frameworks, while case studies and group discussions will encourage students to apply their knowledge to real-world scenarios. Interactive workshops will provide opportunities to explore AI tools and datasets, fostering digital literacy and analytical skills.
Students will also be encouraged to reflect on their own professional contexts and consider how AI might influence their future roles. Through collaborative learning and interdisciplinary dialogue, the module aims to build confidence in navigating complex technological landscapes and making informed, ethical decisions.
On successful completion of the module students will be able to:
1. Critically evaluate the principles and methodologies of artificial intelligence (AI) as applied to healthcare contexts, including diagnostic imaging, clinical decision support, and patient monitoring systems.
2. Analyse the ethical, legal, and societal implications of AI in healthcare, with particular attention to issues of bias, transparency, accountability, and patient consent.
3. Assess the impact of AI technologies on healthcare delivery, professional roles, and patient outcomes, drawing on current research, case studies, and interdisciplinary perspectives.
On successful completion of the module students will be able to:
1. Apply advanced digital literacy skills to critically assess and interpret AI tools and datasets used in healthcare settings.
2. Demonstrate ethical reasoning and decision-making in evaluating the deployment of AI technologies in clinical practice.
3. Communicate complex AI-related concepts and implications effectively to diverse professional audiences, including clinicians, technologists, and patients.
4. Collaborate across disciplinary boundaries to explore the integration of AI in healthcare, drawing on perspectives from medicine, radiography, and physical sciences.
5. Critically reflect on personal and professional development in relation to emerging AI competencies and their relevance to future healthcare roles.
| Delivery type | Number | Length hours | Student hours |
|---|---|---|---|
| Practical (computer based) | 2 | 2 | 4 |
| Lecture | 10 | 1 | 10 |
| Seminar | 6 | 1 | 6 |
| Independent online learning hours | 40 | ||
| Private study hours | 90 | ||
| Total Contact hours | 20 | ||
| Total hours (100hr per 10 credits) | 150 | ||
Students will be supported by formative problem sheets and short answer questions to assist students to gauge their progress. Class group activities will also provide opportunities for peer and tutor feedback.
| Assessment type | Notes | % of formal assessment |
|---|---|---|
| Report | Report advising hospital on the procurement of an AI system | 100 |
| Problem Sheet | Short answer questions and problem sheets | 0 |
| Total percentage (Assessment Coursework) | 100 | |
The self-assessment formative work will be topic based, providing students with either a problem sheet (with answers provided at a later date), or short answer/MCQs automatically marked via Blackboard tests.
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Last updated: 22/05/2026
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