View Specification DocumentApplied GenAI and Data-Informed Practice in Anatomical Imaging 4C option BIOL4275
- Academic Session: 2026-27
- School: School of Medicine Dentistry and Nursing
- Credits: 20
- Level: Level 4 (SCQF level 10)
- Typically Offered: Semester 2
- Available to Visiting Students: Yes
- Collaborative Online International Learning: No
- Curriculum For Life: No
Short Description
Artificial intelligence, including generative AI (genAI), and data-informed practices increasingly support research design and clinical decision-making, service planning and resource allocation within healthcare. The course includes an overview of key anatomical imaging techniques, their applications, strengths and limitations, and provides a real-world, application-focused exploration of how these techniques are supported by AI- and data-informed approaches. Students will examine when AI tools add value, where limitations and risks require human oversight and when hybrid human-AI approaches are most appropriate within anatomical imaging settings. The course is designed to enhance employability by developing applied decision-making, critical reasoning and professional communication skills relevant to careers in healthcare, biomedical research, imaging services and digital health innovation. The course does not focus on imaging physics, programming or algorithm development. Instead, it prioritises applied thinking, critical appraisal of research evidence and responsible data stewardship in the context of anatomical imaging practice.
Timetable
Normally, 3 hours of teaching on Thursdays in Semester 2.
Requirements of Entry
Normally, only available to final-year Life Sciences students in the Human Life Sciences programme. Visiting students may be allowed to enrol, at the discretion of the Life Sciences Chief Adviser and the Course Coordinator.
Excluded Courses
None
Assessment
The course will be assessed by in-course assessments comprising the following: in-class test (50%), scientific poster (25%) and presentation (25%).
Intended Learning Outcomes of Course
By the end of this course, learners will be able to:
■ Critically evaluate the applications, usefulness, benefits and limitations of different anatomical imaging techniques
■ Compare and contrast AI-supported and human-led approaches, identifying when each is most appropriate and when hybrid human-AI approaches offer added value in anatomical imaging practices
■ Systematically and critically appraise the evidence underpinning AI- and data-informed anatomical imaging practices
■ Discuss the ethical, equity, data governance, accountability and sustainability considerations associated with AI- and data-informed anatomical imaging practices