Health and Wellbeing

 

 

The Health and Wellbeing theme invites you to tackle a critical challenge at the intersection of technical capability and human meaning: how can artificial intelligence genuinely serve patients, carers, communities, and healthcare professionals whilst respecting the interpretive complexity of health and illness? 

Healthcare represents a quintessential example of AI's "qualitative turn", where success demands more than technical performance. Health and illness are experienced through rich, sensory, emotional narratives that resist reduction to data points. Your research could explore how to preserve this interpretive depth whilst leveraging AI's capabilities, moving beyond narrow operational metrics towards systems that engage meaningfully with cultural complexity, embodied experience, and multiple valid perspectives on wellbeing. 

This theme responds to urgent risks identified in current AI development: the homogenisation problem, where dominant architectures risk entrenching health inequalities by reinforcing narrow models of health and healing; the transformation of human cognition, where AI might diminish rather than enhance clinical judgment and patient agency; and the implementation gap, where 90% of health AI remains stuck in pilots partly because systems fail to engage with contextual realities. 

Your doctoral research could explore interpretive AI approaches that represent diverse perspectives on health rather than monolithic outputs, human-AI ensemble methods that enhance collaborative intelligence between patients, clinicians, and AI systems, or alternative evaluation frameworks that assess cultural sensitivity and contextual appropriateness alongside technical accuracy. You might propose novel approaches to participatory health AI design, examining how communities can shape systems from ideation through deployment, or investigate how to bridge global health models with local, cultural, and political specificities that shape real implementation. 

Place-based and Regional Context 

The North East offers exceptional opportunities for your citizen-centred health AI research, combining significant healthcare challenges with innovative partners and diverse communities. The region faces higher-than-average chronic disease rates, substantial socioeconomic deprivation, and significant health inequalities (life expectancy gaps between most and least deprived areas exceed ten years). The ageing population creates both challenges and opportunities for research on AI supporting active ageing. 

NHS North East and North Cumbria serves 3.2 million people, actively pursuing digital transformation and AI adoption. Academic Health Science Network NE&NC accelerates innovation adoption with focus on health inequalities. Regional partners include Sunderland Software City, Innovation SuperNetwork, and diverse communities in former mining areas, coastal towns, and urban centres. This combination makes the Northeast ideal for your research investigating whether health AI reduces or amplifies inequalities, enabling you to generate locally relevant and nationally applicable insights through your doctoral work. 

Relevant Partner Organisations 

Students on this theme will have opportunities to work with partners spanning healthcare, government, technology, and community sectors. NHS North East and North Cumbria and Academic Health Science Network NE&NC can provide implementation contexts and clinical partnerships. Cabinet Office, DSIT, and UK Health Security Agency offer pathways to policy influence. Technology partners include Google, Nokia Bell Labs, Thoughtworks, and Yoti. Community connections through VONNE, Trussell Trust, and Newcastle City Council enable citizen voices to shape your research. NHS Business Services Authority offers large-scale data infrastructure research opportunities, while academic partners (City University of London, UBC, UCD) enable collaborative projects addressing implementation, equity, and governance challenges. 

The Psychology and Communication Technology Lab (PaCT Lab, www.pactlab.co.uk) specialises in health technology research including, but not limited to, remote healthcare technologies for eating disorders (see RHED: Research in Health Technologies for Eating Distress Research Group, www.rhhed-research.uk) and stigmatised conditions. PaCT Lab is led by CCAI Co-I, Dr Dawn Branley-Bell. 

Related Articles/Reading 

Human-AI Collaboration in Clinical Decision Making 

• Zhang et al. (2024). Rethinking Human-AI Collaboration in Complex Medical Decision Making: A Case Study in Sepsis Diagnosis. CHI '24. 
• Rajashekar et al. (2024). Human-Algorithmic Interaction Using a Large Language Model-Augmented Artificial Intelligence Clinical Decision Support System. CHI '24. 
• (2023). Healthcare AI Treatment Decision Support: Design Principles to Enhance Clinician Adoption and Trust. CHI '23. 
• (2023). Ignore, Trust, or Negotiate: Understanding Clinician Acceptance of AI-Based Treatment Recommendations in Health Care. CHI '23. 
• (2023). Assertiveness-based Agent Communication for a Personalised Medicine on Medical Imaging Diagnosis. CHI '23. 
• (2024). Multimodal Healthcare AI: Identifying and Designing Clinically Relevant Vision-Language Applications for Radiology. CHI '24. 
• Yang et al. (2024). A Human-AI Collaborative System to Support Mitosis Assessment in Pathology. IUI '24. 
• (2025). Will Health Experts Adopt a Clinical Decision Support System for Game-Based Digital Biomarkers? Investigating the Impact of Different Explanations on Perceived Ease-of-Use, Perceived Usefulness, and Trust. IUI '25. 

Mental Health AI and Ethics 

• (2024). Patient Perspectives on AI-Driven Predictions of Schizophrenia Relapses: Understanding Concerns and Opportunities for Self-Care and Treatment. CHI '24. 
• (2024). Societal-Scale Human-AI Interaction Design? How Hospitals and Companies are Integrating Pervasive Sensing into Mental Healthcare. CHI '24. 
• (2022). Exploring the Effects of AI-assisted Emotional Support Processes in Online Mental Health Community. CHI EA '22. 

Participatory Design and Co-Creation 

• (2022). Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains. CHI EA '22. 
• (2024). Sketching AI Concepts with Capabilities and Examples: AI Innovation in the Intensive Care Unit. CHI '24. 
• (2023). A Patient Centred Approach to Rare Disease Technology. CHI EA '23. 
• (2025). Empowering Social Service with AI: Insights from a Participatory Design Study with Practitioners. CHI EA '25. 

Health Equity and Algorithmic Fairness 

• (2025). Participatory AI Considerations for Advancing Racial Health Equity. CHI '25. 

Ageing, Accessibility, and Inclusive Design 

• (2024). Toward Making Virtual Reality (VR) More Inclusive for Older Adults: Investigating Aging Effect on Target Selection and Manipulation Tasks in VR. CHI '24. 
• (2024). Exploring the Opportunity of Augmented Reality (AR) in Supporting Older Adults to Explore and Learn Smartphone Applications. CHI '24. 
• (2025). Designing Conversational AI for Aging: A Systematic Review of Older Adults' Perceptions and Needs. CHI '25. 
• (2025). Designing Accessible Audio Nudges for Voice Interfaces. CHI '25. 

Implementation Science and Evaluation 

• Zając et al. (2023). Clinician-facing AI in the Wild: Taking Stock of the Sociotechnical Challenges and Opportunities for HCI. ACM Transactions on Computer-Human Interaction. 
• (2023). Using Thematic Analysis in Healthcare HCI at CHI: A Scoping Review. CHI '23. 
• (2025). A Mixed Methods Formative Evaluation of the United Kingdom National Health Service Artificial Intelligence Lab. npj Digital Medicine. 

Large Language Models and Foundation Models in Healthcare 

• (2023). Foundation Models in Healthcare: Opportunities, Risks & Strategies Forward. CHI EA '23. 

Wearable and Sensing Technologies 

• Zhu et al. (2023). BioWeave: Weaving Thread-Based Sweat-Sensing On-Skin Interfaces. UIST '23. 

UK Policy Documents 

• National Health Service (2025). NHS 10 Year Health Plan. 
• UK Government (2023). A Pro-innovation Approach to AI Regulation. 
• Medicines and Healthcare products Regulatory Agency (2024). MHRA AI Regulatory Strategy: Ensuring Patient Safety and Industry Innovation into 2030. 
• UK Government (2025). National Commission into the Regulation of AI in Healthcare. 
• The Health Foundation (2023). Priorities for an AI in Health Care Strategy. 

EU Policy Documents 

• European Union (2024). Regulation (EU) 2024/1689 on Artificial Intelligence (AI Act). 
• European Commission (2025). European Health Data Space Regulation. 
• European Commission (2025). Apply AI Strategy. 
• European Medicines Agency (2024). Reflection Paper on the Use of Artificial Intelligence (AI) in the Medicinal Product Lifecycle. 
• European Commission High-Level Expert Group on AI (2019). Ethics Guidelines for Trustworthy AI. 

Implementation Guidance 

• Navarro et al. (2024). Navigating the European Union Artificial Intelligence Act for Healthcare. npj Digital Medicine. 

Foundational Perspectives on AI Development 

• Hemment, D., Kommers, C., et al. (2025). Doing AI Differently: Rethinking the Foundations of AI via the Humanities. White Paper. The Alan Turing Institute.