| Unit name | Responsible Innovation and Adoption of AI for Health |
|---|---|
| Unit code | BRMSM0098 |
| Credit points | 20 |
| Level of study | M/7 |
| Teaching block(s) |
Teaching Block 2 (weeks 13 - 24) |
| Unit director | Dr. Jon Lees |
| Open unit status | Not open |
| Units you must take before you take this one (pre-requisite units) |
None |
| Units you must take alongside this one (co-requisite units) |
None |
| Units you may not take alongside this one |
None |
| School/department | Bristol Medical School |
| Faculty | Faculty of Health and Life Sciences |
Why is this unit important?
AI is transforming healthcare, with even greater potential on the horizon. However, these advances also present significant challenges, including issues of usability, equity, safety, and trust. Addressing these concerns is essential for the responsible and effective adoption of AI in healthcare systems. There is a significant gap between development of an AI medical/health technology to proving its efficacy and safety and implementing it successfully. This unit covers the phases of development to implementation and adoption in a responsible manner.
In this unit you will work with students from diverse academic and professional backgrounds in solving real-world healthcare problems in multidisciplinary teams. Through hands-on work and critical analysis, you will explore both the opportunities and limitations of AI and digital health in clinical practice. Emphasis is placed on developing responsible, inclusive, and sustainable innovations. Throughout the unit, you will consider the broad range of technical, ethical, societal, and healthcare system factors that influence the successful integration of AI technologies, ensuring they are designed to benefit all stakeholders and enhance the chances of adoption into practice.
How does this unit fit into your programme of study?
This unit builds on your prior learning by applying foundational AI knowledge to realistic, healthcare-focused case studies. Framed within multiple health contexts, the unit will challenge you to consider the diverse perspectives. By bridging theory and practice, the unit prepares you for your upcoming MSc project, where you will address real-world problems using insights and methods developed here.
An overview of content
You will work through various deep dives into digital/AI health case studies, progressing in complexity and grounded in real-world scenarios. You will analyse and design digital/AI solutions while considering clinical challenges, digital inclusion and exclusion, global healthcare differences, business models, and the broader impact of AI on patients and health systems. Key topics include ethical considerations, medical device regulation, research methods for evaluating digital/AI health technologies, design, technology adoption and patient and public involvement (PPI).
Teaching will be delivered through a combination of synchronous and asynchronous sessions, including pre-recorded video lectures, on-campus lectures and seminars, and formative self-directed exercises. There will be an emphasis on group work through case-based learning (CBL) reflecting the digital health values of promoting effective interdisciplinary working.
How will students, personally, be different as a result of the unit
You will develop practical skills in problem-solving, within diverse health systems, and working in a multiprofessional team. You will gain a deep understanding of the full lifecycle of digital/AI health technologies from design and development to evaluation and implementation while learning to navigate ethical and regulatory frameworks, including how to obtain approvals and assess real-world impact. By the end of the unit, you will think more critically about the role of AI in healthcare, approach challenges with a multidisciplinary mindset, and be equipped to design, evaluate, and implement responsible AI solutions that address both clinical and societal needs.
Learning Outcomes
Teaching will be delivered through a combination of synchronous and asynchronous sessions, including pre-recorded video lectures, on-campus lectures and seminars, and formative self-directed exercises. There will be an emphasis on group work, reflecting the multi-disciplinary nature of digital/AI in health and promoting effective interdisciplinary working.
Formative
Teaching will be delivered through a combination of synchronous and asynchronous sessions, including pre-recorded video lectures, on-campus lectures and seminars, and formative self-directed exercises. There will be an emphasis on group work, reflecting the values of promoting effective interdisciplinary working. You will produce deliverables for each of the case studies considered and facilitators will give feedback throughout this process helping to prepare you for the summative assessments.
Summative
This unit will be assessed by two coursework assignments.
An exam (50%) with essay and/or short answer questions
Group video presentation (50%) in which you will work together to produce a recorded presentation that explores a digital/AI health case study, in depth*.
When assessment does not go to plan
If you do not pass the unit, you will normally be given the opportunity to take a reassessment as per the Regulations and Code of Practice for Taught Programmes. Re-assessment takes a similar form to the original summative assessment but adapted for individual students to be able to complete the deliverables. In the case of the group video presentation this will be as an individual oral presentation based on the original assignment, including a short reflection on how multi-disciplinary teamwork would have aided this task.
If this unit has a Resource List, you will normally find a link to it in the Blackboard area for the unit. Sometimes there will be a separate link for each weekly topic.
If you are unable to access a list through Blackboard, you can also find it via the Resource Lists homepage. Search for the list by the unit name or code (e.g. BRMSM0098).
How much time the unit requires
Each credit equates to 10 hours of total student input. For example a 20 credit unit will take you 200 hours
of study to complete. Your total learning time is made up of contact time, directed learning tasks,
independent learning and assessment activity.
See the University Workload statement relating to this unit for more information.
Assessment
The assessment methods listed in this unit specification are designed to enable students to demonstrate the named learning outcomes (LOs). Where a disability prevents a student from undertaking a specific method of assessment, schools will make reasonable adjustments to support a student to demonstrate the LO by an alternative method or with additional resources.
The Board of Examiners will consider all cases where students have failed or not completed the assessments required for credit.
The Board considers each student's outcomes across all the units which contribute to each year's programme of study. For appropriate assessments, if you have self-certificated your absence, you will normally be required to complete it the next time it runs (for assessments at the end of TB1 and TB2 this is usually in the next re-assessment period).
The Board of Examiners will take into account any exceptional circumstances and operates
within the Regulations and Code of Practice for Taught Programmes.
