AI and healthcare research: sharing sector learning

In this blog, Robyn Cooke, Policy and Research Manager at the Council of Deans of Health, explores how AI is transforming healthcare research and education, highlighting both its potential to enhance innovation and the need to address risks such as bias and ethical use. Robyn also highlights the Council’s principles for Gen AI, emphasising transparency, collaboration, and ongoing learning across the sector.

Few areas of society have remained untouched by the so‑called ‘AI revolution’, with its impact especially pronounced in healthcare and higher education. The NHS’s 10‑year plan, for example, sets out ambitions to develop ‘the most AI‑enabled workforce in the world’, while debates around the ethical use of AI and its implications for academic integrity are now central within universities.

Healthcare research has the potential to greatly enhance population health and well-being, and technologies such as AI can play a key role in driving and supporting innovation. AI can be utilised by learners and researchers to accelerate literature reviews, data analysis and interdisciplinary work but there does also need to be consideration of issues such as bias and authorship. We have explored the issue of bias in AI systems in a podcast which unpacks what it means in the context of healthcare education and research.

The Council has published a report on ‘The Principles for the use of Generative AI in healthcare education’, however these principles are completely applicable to healthcare research. The report outlines eight high level principles for integrating AI into healthcare education and research, recognising its potential to enhance critical thinking skills and enhance student and staff research capabilities. Human oversight, interdisciplinary collaboration and a strong service user voice were central to the principles.

The principles were developed through a multi-professional and collaborative process, and you can hear more about the research methodology in this podcast.

A supplementary document has also been published, providing illustrative examples to demonstrate each of the principles and showing how AI can support innovative approaches to education and research.

Example 13 demonstrates how transparent use of AI can support students to access and engage with research articles with learners expressing better understanding of the article and a greater willingness to engage with the detail of the article methodology and its application to practice.

The aim of these publications is to add to the knowledge base and share sector learning. However, with the rapidness of the AI evolution we realise that the principles and the illustrative examples will need to be reviewed to ensure we are keeping pace with changes. We want to continue to work with the sector to ensure that the use of AI in healthcare research is done in an effective, transparent and human centred way.