Artificial Intelligence for Healthcare: Generative AI and Trustworthy Machine Learning for Early Disease Detection and Clinical Decision Support

About the project

About the Project Artificial Intelligence (AI) is transforming healthcare by enabling earlier disease detection, improving diagnostic accuracy, and supporting clinical decision-making. Recent advances in Generative AI, Machine Learning, Deep Learning, and Large Language Models (LLMs) offer significant opportunities to analyse complex healthcare data and provide personalised, data-driven insights. However, challenges related to explainability, fairness, trust, and responsible deployment continue to limit the widespread adoption of AI in healthcare. This PhD project aims to develop and evaluate innovative Generative AI and Trustworthy Machine Learning approaches for early disease detection and clinical decision support. The successful candidate will investigate how AI can be used to analyse healthcare data, generate clinically meaningful insights, and support healthcare professionals in making informed decisions. The research may involve the use of electronic health records, clinical notes, laboratory results, medical imaging, and other healthcare data sources. A key focus will be the development of AI systems that are accurate, explainable, transparent, fair, and trustworthy. The project will explore topics such as Explainable AI (XAI), Human-AI Collaboration, Clinical Decision Support Systems, Disease Risk Prediction, Responsible AI, and Multimodal AI for healthcare applications. The successful applicant will work at the intersection of Artificial Intelligence, Data Science, Digital Health, Health Informatics, and Healthcare Innovation. The project offers opportunities for interdisciplinary collaboration and the potential to contribute to real-world healthcare challenges, including chronic disease management, cancer detection, cardiovascular health, and preventive medicine. This PhD provides an exciting opportunity to contribute to cutting-edge research in AI for Healthcare and develop next-generation technologies that support improved patient outcomes, personalised healthcare, and evidence-based clinical decision-making. Eligibility / Candidate Profile: Candidates are expected to hold, or be close to completing, a minimum Upper Second-Class Honours degree (2:1) or equivalent in a relevant subject area, such as Computer Science, Informatics, Artificial Intelligence, Machine Learning, Data Science, Health Informatics, Biomedical Engineering, or a closely related discipline. A master’s degree (MSc, MEng, MA, or equivalent) and/or relevant professional or research experience in a related field is highly desirable. Applicants from other academic or professional backgrounds will also be considered, provided they can demonstrate relevant expertise aligned with the research theme. How to apply Formal applications can be made via the University of Bradford web site ; applicants will need to register an account, select 'Postgraduate Research' as the course and then use the keywords 'computer science'. Applicants should then specify the project title in the 'Research Proposal' section. About the University of Bradford Bradford is a research-active University supporting the highest-quality research. We excel in applying our research to benefit our stakeholders by working with employers and organisations world-wide across the private, public, voluntary and community sectors and actively encourage and support our postgraduate researchers to engage in research and business development activities. Positive Action Statement At the University of Bradford our vision is a world of inclusion and equality of opportunity, where people want to, and can, make a difference. We place equality and diversity, inclusion, and a commitment to social mobility at the centre of our mission and ethos. In working to make a difference we are committed to addressing systemic inequality and disadvantages experienced by Black, Asian and Minority Ethnic staff and students. Under sections 158-159 of the Equality Act 2010, positive action can be taken where protected group members are under-represented. At Bradford, our data show that people from Black, Asian, and Minority Ethnic groups who are UK nationals are significantly under-represented at the postgraduate researcher level. These are lawful measures designed to address systemic and structural issues which result in the under-representation of Black, Asian, and Minority Ethnic students in PGR studies.

Research areas