Health and social care
Our research to support the safe introduction of autonomous technologies into health and social care covers underlying technical research and work from demonstrators on areas such as assistive robots and ambulance response.
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.
A number of AAIP-funded industry projects are in the health and social care domain. These are shown below. We have funded projects in a number of other domains and you can also view all of the AAIP-funded demonstrator projects.
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.
- 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 (free access until 2 July).
- Jia, Y., Lawton, T., Burden, J., McDermid, J., and Habli, I. "Safety-Driven Design of Machine Learning for Sepsis Treatment" in Journal of Biomedical Informatics, May 2021
On the blog
Our other research domains
Learn what research is taking place in other domains that might be transferable to your work.