Study information

AI in Healthcare - 2025 entry

MODULE TITLEAI in Healthcare CREDIT VALUE15
MODULE CODECOMM118 MODULE CONVENERUnknown
DURATION: TERM 1 2 3
DURATION: WEEKS 11
Number of Students Taking Module (anticipated) 40
DESCRIPTION - summary of the module content

AI has transformed industries around the world and has the potential to reshape the field of healthcare. In this module, you will learn fundamental AI concepts that can benefit the healthcare industry, including for example, machine learning, text analysis, and computer vision. You will study the current and future applications of AI in healthcare, such as enhancing patient care and improving diagnostic accuracy. You will attend lectures complemented by lab sessions or discussion, where you will apply AI techniques to real-world healthcare datasets and analyse case studies. This module is suitable for Computer Science, Mathematics and Engineering students and any students with experience in programming and fundamental machine learning concepts.

Pre-requisite modules: COMM113 Deep Learning

AIMS - intentions of the module

This module aims to provide you with knowledge and skills of how AI technologies can be applied to address challenges in healthcare. You will explore key concepts such as machine learning, text analysis, and computer vision, and examine their applications in medical data analysis, diagnosis, and treatment planning. You will learn to analyse real-world healthcare challenges, including predictive modelling, medical imaging, and personalized treatment, and formulate them as machine learning problems. You will also evaluate the performance and impact of AI across various healthcare applications, while considering the ethical and regulatory aspects of deploying AI in healthcare settings. Through hands-on projects and case studies, you will gain practical experience in applying AI techniques to healthcare problems, preparing you for a career in AI-driven healthcare innovation.

INTENDED LEARNING OUTCOMES (ILOs) (see assessment section below for how ILOs will be assessed)

On successful completion of this module you should be able to:

Module Specific Skills and Knowledge:

  1. Explain the AI applications and challenges within the healthcare industry, including for example medical imaging and diagnosis, administrative automation, and predictive analysis.

  2. Formulate real-world healthcare challenges as machine learning problems.

  3. Design AI-based solutions to address healthcare challenges, for example, AI-assisted diagnosis, improving patient outcomes, and optimising hospital operations.

  4. Evaluate the designed AI solutions for limitations, strengths, improvements required, etc.

Discipline Specific Skills and Knowledge:

  1. Critically analyse and evaluate the performance and impact of AI in various applications.

  2. Identify the compromises and trade-offs which must be made when translating AI theory into practice.

Personal and Key Transferable/ Employment Skills and Knowledge:

  1. Effectively communicate insights and evaluations drawn from research papers.   

SYLLABUS PLAN - summary of the structure and academic content of the module
Concepts and Theoretical Foundations
Introduction to AI for healthcare.
Introduction to deep learning and advanced model structures.
Healthcare data, for example, quality and quantity.
 
Implementation and Practical Techniques
Healthcare use cases.
Model development and optimization for healthcare tasks.
 
Analysis and Evaluation
Quantitative and qualitative evaluation techniques, for example, performance metrics and interpretability in healthcare.
Ablation study.
Real-world case studies and advanced applications.
 
LEARNING AND TEACHING
LEARNING ACTIVITIES AND TEACHING METHODS (given in hours of study time)
Scheduled Learning & Teaching Activities 33 Guided Independent Study 117 Placement / Study Abroad 0
DETAILS OF LEARNING ACTIVITIES AND TEACHING METHODS
Category  Hours of study time  Description 
Scheduled Learning & Teaching activities 22 Lectures
Scheduled Learning & Teaching activities 11 Workshops/tutorials
Guided independent study 45 Coursework preparation and completion
Guided independent study 72 Wider reading and self-study

 

ASSESSMENT
FORMATIVE ASSESSMENT - for feedback and development purposes; does not count towards module grade
Form of Assessment Size of the assessment e.g. duration/length ILOs assessed Feedback method
Practical Exercises   10 All  Answers to exercises and oral feedback 

 

SUMMATIVE ASSESSMENT (% of credit)
Coursework 30 Written Exams 70 Practical Exams 0
DETAILS OF SUMMATIVE ASSESSMENT
Form of Assessment % of credit Size of the assessment e.g. duration/length ILOs assessed  Feedback method
Continuous assessment 30  30 hours  All  Written 
Written exam – closed book 70  2 hours  1, 3, 4, 5, 6  Written 

 

DETAILS OF RE-ASSESSMENT (where required by referral or deferral)
Original form of assessment Form of re-assessment  ILOs re-assessed Time scale for re-assessment
Continuous assessment Continuous assessment  All  Referral/deferral period 
Written exam – closed book Written exam – closed book 1, 3, 4, 5, 6  Referral/deferral period 

 

RE-ASSESSMENT NOTES

Reassessment will be by coursework/quiz in the failed or deferred element only. For referred candidates, the module mark will be capped at 50%. For deferred candidates, the module mark will be uncapped.

RESOURCES
INDICATIVE LEARNING RESOURCES - The following list is offered as an indication of the type & level of
information that you are expected to consult. Further guidance will be provided by the Module Convener

Basic reading:

  • Rubeis, G., 2024. Ethics of medical AI. Cham: Springer.

  • Smuha, N.A., 2025. The Cambridge Handbook of the Law, Ethics and Policy of Artificial Intelligence. Chapter 15: AI and Healthcare Data.

Web-based and electronic resources: 

  • ELE 

Reading list for this module:

There are currently no reading list entries found for this module.

CREDIT VALUE 15 ECTS VALUE 7.5
PRE-REQUISITE MODULES COMM113
CO-REQUISITE MODULES
NQF LEVEL (FHEQ) 7 AVAILABLE AS DISTANCE LEARNING No
ORIGIN DATE Monday 11th November 2024 LAST REVISION DATE Wednesday 6th August 2025
KEY WORDS SEARCH Healthcare, AI, machine learning

Please note that all modules are subject to change, please get in touch if you have any questions about this module.