Health and Wellbeing

The health and wellbeing theme explores the intersection of artificial intelligence (AI) and citizen-centred healthcare. AI provides profound, exciting new opportunities for innovation and efficiency. One notable area of transformation lies in diagnostic capabilities (e.g., the use of AI to quickly analyse vast datasets to detect patterns and anomalies in medical images). AI has the potential to facilitate early detection of diseases, whilst reducing burden on healthcare professionals. It also has the potential to model and predict disease progression, aiding the formation of personalised patient-focused healthcare plans. 

AI has also significantly influenced the broader landscape of health, extending into the realm of wellbeing. For example, the application of AI-driven digital mental health tools, which aim to offer personalised interventions for mental health support. Many existing tools are incorporated into mobile devices and wearables (e.g., mobile apps, smartwatches). These applications leverage machine learning to adapt to individual needs, aiming to provide timely insights and coping strategies to enhance well-being – this can include insights around stress management, sleep improvement, and increased cardiovascular health. Virtual mental health assistants and chatbots have also emerged as accessible resources, offering support and information. Chatbots can also potentially promote help seeking for stigmatised or embarrassing health conditions (additional info).

However, as AI increasingly permeates health and wellbeing applications, researchers grapple with ethical considerations. Privacy concerns and data security are paramount. Researchers must also grapple with the ethical implications of AI algorithms in decision-making processes, particularly when these decisions impact on an individual’s health or wellbeing. It is possible for AI to be inaccurate or to not behave in the predicted manner (additional info) and this can have major consequences.

Striking a balance between the potential benefits and challenges of AI is an ongoing challenge. There is a need for research into the interpretability and explainability of AI algorithms (additional info).

Understanding how algorithms reach their conclusions is crucial for gaining user trust (e.g., healthcare professionals and patients). It is also a vital requirement if any human user is to be able to evaluate the performance and accuracy of the system. Additionally, issues of bias in AI, stemming from biased training datasets, raise questions about fairness and equity (see the UK government's white paper on AI regulation).

In this evolving landscape, interdisciplinary research becomes essential, bringing together experts from psychology, computer science, health, law, and beyond to delve into the nuanced challenges and opportunities. Therefore, students in this theme can expect to explore a range of timely, fascinating topics from multiple perspectives.

Experts

• Dr Dawn Branley-Bell
• Prof Pam Briggs

Related Peak of Research Excellence

• Computerised Society and Digital Citizens

Suggested Literature

• Branley-Bell et al. (2023). Chatbots for embarrassing and stigmatizing conditions: could chatbots encourage users to seek medical advice? Frontiers in Communication
• Branley-Bell, D., Whitworth, R., & Coventry, L. (2020). User Trust and Understanding of Explainable AI: Exploring Algorithm Visualisations and User Biases. Human-Computer Interaction International (HCII) Conference 2020 [Best HCI paper award]. Springer.
• Damij, N. and Bhattacharya, S. (2022). "The Role of AI Chatbots in Mental Health Related Public Services in a (Post)Pandemic World: A Review and Future Research Agenda". IEEE Technology and Engineering Management Conference.
• Janjeva, A.,Calder, M., and Oswald, M. (May 2023). "Privacy Intrusion and National Security in the Age of AI: Assessing proportionality of automated analytics". Centre for Emerging Technology and Security (CETaS) Research Reports.
• Manickam P, Mariappan SA, Murugesan SM, Hansda S, Kaushik A, Shinde R, Thipperudraswamy SP (2022). Artificial Intelligence (AI) and Internet of Medical Things (IoMT) Assisted Biomedical Systems for Intelligent Healthcare. Biosensors 12(8):562.
• Naik et al. (2022). Legal and Ethical Consideration in Artificial Intelligence in Healthcare: Who Takes Responsibility? Frontiers in Surgery
• Saraswat et al. (2022). Explainable AI for Healthcare 5.0: Opportunities and Challenges. IEEE Access (10), doi: 10.1109/ACCESS.2022.3197671

*if you are struggling to access any of the suggested literature, then please contactccai.cdt@northumbria.ac.uk