Key Recommendations for Safe and Effective Medical AI Implementation - Video

A new set of recommendations published in The Lancet Digital Health and NEJM AI aims to help improve the way datasets are used to build Artificial intelligence (AI) health technologies and reduce the risk of potential AI bias.

An international initiative called ‘STANDING Together (STANdards for data Diversity, INclusivity and Generalisability)’ has published recommendations as part of a research study involving more than 350 experts from 58 countries. These recommendations aim to ensure that medical AI can be safe and effective for everyone. They cover many factors which can contribute to AI bias, including:

• Encouraging medical AI to be developed using appropriate healthcare datasets that properly represent everyone in society, including minoritised and underserved groups;

• Helping anyone who publishes healthcare datasets to identify any biases or limitations in the data;

• Enabling those developing medical AI technologies to assess whether a dataset is suitable for their purposes;

• Defining how AI technologies should be tested to identify if they are biased, and so work less well in certain people.

The STANDING Together recommendations aim to ensure that the datasets used to train and test medical AI systems represent the full diversity of the people that the technology will be used for. This is because AI systems often work less well for people who aren’t properly represented in datasets. People who are in minority groups are particularly likely to be under-represented in datasets, so may be disproportionately affected by AI bias. Guidance is also given on how to identify those who may be harmed when medical AI systems are used, allowing this risk to be reduced.

Reference: Tackling algorithmic bias and promoting transparency in health datasets: the STANDING Together consensus recommendations, Alderman, Joseph E et al.The Lancet Digital Health, Volume 0, Issue 0