Research Studentship in Human-AI Collaboration in Medical Imaging

Research Studentship in Human-AI Collaboration in Medical Imaging

3.5-year D.Phil. studentship 

Project: Human-AI Collaboration in Medical Imaging

Supervisors: Prof Alison Noble CBE FRS FREng

Artificial Intelligence (AI)-based systems that support a human in making decisions in a high-risk setting such as healthcare need to be trustworthy and interpretable. If the AI and human make a joint decision, we call this Human-AI collaboration (HAIC). It is an interesting but complex area as to design good HAIC systems it is not just about the computer science of building machine learning models but you need to understand both how an AI system makes a decision and how humans make decisions.

The initial part of this project will investigate learning-to-defer techniques to support decision-making in medical imaging. The definition of clinical exemplars forms part of the research, but potential areas are in fetal medicine, radiology and ultrasound-guided procedures which would build on the extensive experience and multi-modal video datasets already available in the laboratory. Later parts of the project will be guided by the success of the initial work and will focus on technical and inter-disciplinary studies to advance understanding of how assistive-AI systems can communicate and interact with healthcare professionals. As part of this work, there may be an opportunity to collaborate with researchers in psychology or ethics, depending on the interests of the student.

Eligibility

This studentship is open to Home students (full award – home fees plus stipend). There is limited flexibility to support international students. If you are an international student and want to apply for this studentship please contact the supervisor to see whether the flexibility might be available for you.

Award Value

Course fees are covered at the level set for Home students.  The stipend (tax-free maintenance grant) is c. £19,237 p.a. for the first year, and at least this amount for a further two and a half years. 

Candidate Requirements

Prospective candidates will be judged according to how well they meet the following criteria:

·        A first class or strong upper second-class undergraduate honours degree in Engineering, Computer Science, Mathematics, Statistics, or a related field with substantial computational background

·        Research (for instance as part of first degree) or working experience in medical image analysis, computer vision, or machine learning

·        Excellent English written and spoken communication skills

The following skills are desirable but not essential:

·        Ability to program in Python

·        Knowledge of medical imaging physics

·        Knowledge of natural language processing (NLP)

Application Procedure

Informal enquiries are encouraged and should be addressed to Prof Alison Noble ([email protected]).

Candidates must submit a graduate application form and are expected to meet the graduate admissions criteria. Details are available on the course page of the University website.

Please quote 25ENGBM_AN in all correspondence and in your graduate application.

Application deadline: noon on 3 December 2024 (In line with the University admissions deadline set by the University)

Start date: October 2025