PE4S298 - Foundations of Artificial Intelligence in Healthcare 01 Sep 2024 - 31 Aug 2030 | Version 1

Associated Module Information

Module Code: PE4S298
Module Title: Foundations of Artificial Intelligence in Healthcare
Faculty: Faculty of Life Sciences and Education
Faculty Group: Allied Health and Chiropractic
Faculty Sub Group: Clinical Services
Module Leader: Iram Ashraf
Module Team: Faisal Rashid
First Intended Intake: SEP 2024 Final Year of Intake: 2029
Date Closed:
Credit Value: 20 Credit Level: 7
Language: English
Percentage of Module Taught in Welsh: 0
Equivalent Module:
HECOS codes: 100260 - healthcare science
HECOS Code Weighting: 100

Document Version Information

Version 1
Valid From 01 Sep 2024
Valid To 31 Aug 2030

Module Aims

This module aims to introduce students to the fundamentals of Artificial Intelligence (AI) and its applications in medicine. Students will gain an understanding of AI technologies that can be leveraged to enhance diagnostic accuracy, treatment planning, and patient outcomes within healthcare settings.  

Content Summary

Indicative content to include topics outlined below and/or any other relevant current topics to fulfil the module aims and learning outcomes:  

Week 1 - Introduction to AI in Healthcare 

Week 2 - Core AI Technologies in Healthcare 

Week 3 - Managing Medical Data 

Week 4 - AI in Clinical Decision Making 

Week 5 - AI in Medical Research 

Week 6 - Challenges in AI for Healthcare

Learning and Teaching Methods

Activity Type Hours
Seminars 40
Independent Study 80
Directed Study (including online independent learning) 40
Problem/Challenge based learning 40
Total Hours Selected 200

Learning Outcomes

# Learning Outcome
LO1 Critically analyse evidence-based research, methods, and challenges associated with applying artificial intelligence technologies in healthcare settings.
LO2 Develop a systematic understanding of evidence-based solutions and recommendations that leverage AI to improve patient outcomes, maximise the value of medical research, and address ethical challenges surrounding the implementation of artificial intelligence technology and principles in healthcare.

Module Requisites

N/A

Assessment Criteria

Assessment Category Assessment Type Description Duration Word Count Weight (%) Best of? Pass Mark
Asynchronous Assessment Case study 1 A concentrated inquiry into a single case or subject Learna Case-based scenarios and a discussion forum related to artificial intelligence in medicine. 0 2500 40 No 40
Asynchronous Assessment Project 1 A detailed analysis of a topic, involving some original research undertaken by the candidate who makes use of data and/or primary sources Learna Completion of an individual/group task related to artificial intelligence in medicine 0 1000 20 No 40
Asynchronous Assessment Self Reflective Assessment 1 A personal record of a student’s learning experiences. It requires students to record and reflect upon their observations and responses to situations, which can then be used later to explore and analyse ways of thinking and being in context. Generally involves critical diaries, learning logs and written / visual journals Learna Reflective journal. 0 600 10 No 40

Assessment Matrix

Assessment Type Learning Outcomes
LO1 LO2
Case study 1 ✔ ✔
Project 1 ✔ ✔
Self Reflective Assessment 1 ✔ ✔

Reading List

Each module reading list is indicative and will consist of up-to-date peer-reviewed journal articles and studies that are continually refreshed as guidelines change and new treatments and technologies emerge. 

 

Additional reading resources are also supplemented by the tutors during the running of the module in the discussion forum to keep the reading resources current and relevant. 

 

The module reading list is available to the students to access via electronic links on the learning platform (Moodle). 

 

Journals:  

Bajwa, J., Munir, U., Nori, A. and Williams, B., (2021). Artificial intelligence in healthcare: transforming the practice of medicine. Future healthcare journal, 8(2), p.e188. 

 

Davenport, T. and Kalakota, R., (2019). The potential for artificial intelligence in healthcare. Future healthcare journal, 6(2), p.94. 

 

Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, 

G., Thrun, S., & Dean, J. (2019). A guide to deep learning in healthcare. Nature Medicine, 25, 

24–29. https://doi.org/10.1038/s41591-018-0316-z 

 

Hamet, P., & Tremblay, J., 2017. Artificial intelligence in medicine.. Metabolism: clinical and 

experimental, 69S, pp. S36-S40 . https://doi.org/10.1016/j.metabol.2017.01.011. 

 

Obermeyer, Z., & Emanuel, E. J. (2016). Predicting the Future — Big Data, Machine Learning, 

and Clinical Medicine. The New England Journal of Medicine, 375(13), 1216-1219. 

https://doi.org/10.1056/NEJMp1606181 

 

Jiang, F., Jiang, Y., Zhi, H., Dong, Y., Li, H., Ma, S., Wang, Y., Dong, Q., Shen, H. and Wang, Y., (2017). Artificial intelligence in healthcare: past, present and future. Stroke and vascular neurology, 2(4).  

 

Kelly, C.J., Karthikesalingam, A., Suleyman, M., Corrado, G. and King, D., (2019). Key challenges for delivering clinical impact with artificial intelligence. BMC medicine, 17, pp.1-9. 

 

Raghupathi, W., & Raghupathi, V. (2014). Big data analytics in healthcare: promise and 

potential. Health Information Science and Systems, 2(3). https://doi.org/10.1186/2047-2501-2-3  

 

Russell, S., & Norvig, P. (2020). Artificial Intelligence: A Modern Approach (4th ed.). Pearson. 

ISBN: 978-0134610993 

 

Topol, E. (2019). High-performance medicine: the convergence of human and artificial 

intelligence. Nature Medicine, 25, pp. 44-56. https://doi.org/10.1038/s41591-018-0300-7. 

 

Wahl, B., Cossy-Gantner, A., Germann, S., & Schwalbe, N.(2018). Artificial intelligence (AI) 

and global health: how can AI contribute to health in resource-poor settings?. BMJ Global 

Health, 3. https://doi.org/10.1136/bmjgh-2018-000798. 

 

Yu, K., Beam, A., & Kohane, I.(2018). Artificial intelligence in healthcare. Nature Biomedical 

Engineering, 2, pp. 719-731. https://doi.org/10.1038/s41551-018-0305-z.