PATH40050 AI & Digital Pathology: Theory & Practice

Academic Year 2022/2023

This module will introduce students to the rapidly expanding field of Digital Pathology (DP)
The expanding role of DP in telepathology and rapid diagnosis will be highlighted.
The role of DP & Artificial Intelligence (AI) in diagnostic pathology, biomedical research and education will be a fundamental part of the module, in particular its role translating research into clinical practice.
Machine learning algorithms in research will be explored.

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Curricular information is subject to change

Learning Outcomes:

Understanding the role of Digital Pathology in the diagnosis of disease.
Understanding the role of Digital pathology in Education.
Understanding the role of AI in diagnosis & research.

Student Effort Hours: 
Student Effort Type Hours






Computer Aided Lab


Field Trip/External Visits


Specified Learning Activities


Autonomous Student Learning


Online Learning




Approaches to Teaching and Learning:
On line & face to face lectures, visiting laboratories, student presentations, practicals, tutorials, group work 
Requirements, Exclusions and Recommendations

Not applicable to this module.

Module Requisites and Incompatibles
Not applicable to this module.
Assessment Strategy  
Description Timing Open Book Exam Component Scale Must Pass Component % of Final Grade
Attendance: Participation in the "PAIP Challenge: Perineural Invasion in multiple organ cancer 2021".
Varies over the Trimester n/a Standard conversion grade scale 40% No


Portfolio: Brightspace has an ePortfolio; you keep a log tracking progress; including reflection, activity or other materials. You are expected to log at least one artifact for each session (lectures & tutorial) Throughout the Trimester n/a Standard conversion grade scale 40% No


Presentation: Each student is expected to choose a topic from the reading list and present it to the class as a PowerPoint presentation. Coursework (End of Trimester) n/a Standard conversion grade scale 40% No


Project: Design multiple choice questions (20 questions) which cover the entire subject of digital pathology: theory and practice. Coursework (End of Trimester) n/a Standard conversion grade scale 40% No


Carry forward of passed components
Remediation Type Remediation Timing
In-Module Resit Prior to relevant Programme Exam Board
Please see Student Jargon Buster for more information about remediation types and timing. 
Feedback Strategy/Strategies

• Feedback individually to students, on an activity or draft prior to summative assessment
• Feedback individually to students, post-assessment
• Group/class feedback, post-assessment
• Peer review activities
• Self-assessment activities

How will my Feedback be Delivered?

Not yet recorded.

Name Role
Niamh Belton Tutor
Daniele Mario Di Capua Tutor
Professor Aurelie Fabre Tutor
Professor Bill Watson Tutor