Health and social care

This section pulls together all the research taking place in health and social care. It also highlights cross-domain work which will be of use to those working in the sector.

Where possible, a lot of the guidance we publish is intentionally developed to be suitable for use in any domain. You will therefore find key pieces of research below that are suitable for those working in any sector.

Body of Knowledge guidance

You can access entries in the Body of Knowledge related to the safety assurance of autonomous health care systems below, along with useful guidance suitable for all domains. The guidance in the Body of Knowledge is often generalised, enabling you to learn from research undertaken in other domains. You can also access all Body of Knowledge Guidance.

• 1.1 Identifying hazards
• 1.2.1 Considering human/machine interactions
• 1.2 Identifying hazardous system behaviour
• 1.3.1 Validation of safety requirements
• 2.6 Handling change during operation

Latest research papers

Here are the most recent papers based on research in the health and social care domain from the York team and our demonstrators and Fellows. You can also view all of our research papers.

• Laher, S., Brackstone, C., Reis, S., Nguyen, A., White, S., and Habli, I. "Review of the AMLAS methodology for application in healthcare". The paper is on Arxiv. September 2022.
• Sujan, M., White, S., Habli, I., and Reynolds, N. "Stakeholder perceptions of the safety and assurance of artificial intelligence in healthcare" in Safety Science, November 2022
• Festor, P., Jia, Y., Gordon, A.C., Faisal, A.A., Habli, I., and Komorowski, M. "Assuring the safety of AI-based clinical decision support systems: a case study of the AI Clinician for sepsis treatment" in BMJ Health and Care Informatics (July 2022)
• Sujan, M., Pool, R., and Salmon, P. "Eight human factors and ergonomics principles for healthcare artificial intelligence" in BMJ Health & Care Informatics (January 2022)
• Festor, P., Luise, G., Komorowski, M., and Faisal, AA. “Enabling risk-aware reinforcement learning for medical interventions through uncertainty decomposition”  in Interpretable Machine Learning in Healthcare (IMLH) at the ICML 2021 Workshop
• Jia, Y., Kaul, C., Lawton, T., Murray-Smith, R., and Habli, I. "Prediction of weaning from mechanical ventilation using convolutional neural networks" in Artificial Intelligence in Medicine.