Pre-Implementation Evaluation of AI-Supported Menopause Group Consultations

About the project

About the Project Applications are invited for a self-funded, 3 year full-time or 6 year part-time PhD project. The PhD will be based in the School of Computing, Mathematics and Physics and will be supervised by Dr Taiwo Adedeji and Dr Elisavet Andrikopoulou . The project will enjoy support from two Research Institutes: Portsmouth AI and Data Science Centre (PAIDS) and the Institute of Health and Life Sciences (IHLS). The project sits at the intersection of Computer Science, Artificial Intelligence, Health Informatics, and Women's Health. It focuses on the pre-implementation evaluation of AI-supported menopause group consultations. Project Highlights : The work on this project will: Review the evidence base on menopause group consultations and the role of AI/ML in supporting them. Co-design AI-supported group consultation models with patients and clinicians (GPs, pharmacists, and nurses). Develop a pre-implementation evaluation methodology for AI in healthcare group consultations. Project description: Menopause affects approximately half the population yet remains under-researched, under-diagnosed, and inconsistently managed in primary care. Group consultations, in which several patients meet with a clinician, are an emerging NHS model offering scalability, efficiency, and peer support. There is growing interest in using AI/ML methods to support these consultations, for example, by analysing consultation notes/transcripts, audio, and patient-reported outcomes. However, evidence on how to design AI-supported menopause group consultations remains limited. Pre-implementation evaluation is critical. Decisions made before deployment shape clinical safety, equity, acceptability, and the likelihood that AI/ML tools deliver real-world benefit. This PhD focuses on the pre-implementation phase of a broader programme of research, developing an AI evaluation framework for healthcare group consultations, using menopause as the exemplar. The project will combine systematic and scoping reviews of the literature on menopause care, group consultations, and AI in healthcare; qualitative co-design with patients and clinicians (GPs, pharmacists, and nurses); and pre-deployment evaluation of candidate AI/ML methods, such as NLP for transcript analysis, audio analysis, or multimodal models, using existing or proxy data. The research will answer the following overarching questions: What are the needs and concerns of patients and clinicians regarding AI-supported menopause group consultations? Which AI/ML methods are appropriate candidates for this setting, and how should they be evaluated prior to deployment? How can equity, acceptability, safety, and data governance be built into design from the outset? The supervisory team has excellent connections with the BCS, NHS, NICE, and the NHS Primary Care Network, and collaborates with colleagues in Pharmacy and primary care. General admissions criteria You'll need a good first degree from an internationally recognised university (minimum upper second class or equivalent, depending on your chosen course) or a master’s degree in an appropriate subject such as Data Science, AI, or related degree. In exceptional cases, we may consider equivalent professional experience and/or qualifications. English language proficiency at a minimum of IELTS band 6.5 with no component score below 6.0. International students will require a study visa from UKVI to pursue the degree in the UK. If the research is in a sensitive or technological subject, the student may also need to secure an Academic Technology Approval Scheme (ATAS) certificate from the UK Foreign Office. Specific candidate requirements Experience in qualitative methods, co-design, or healthcare research is desirable but not essential. Strong written and oral communication skills and the ability to work with clinical and patient stakeholders are required. How to Apply We’d encourage you to contact Dr Taiwo Adedeji ( taiwo.adedeji@port.ac.uk ) to discuss your interest before you apply, quoting the project code. When you are ready to apply, please follow the ' Apply now ' link on the Computing PhD subject area page and select the link for the relevant intake.. Make sure you submit a personal statement, proof of your degrees and grades, details of two referees, proof of your English language proficiency and an up-to-date CV. Our ‘ How to Apply ’ page offers further guidance on the PhD application process. When applying please quote project code CMP10710529.

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