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AI could worsen healthcare inequalities for UK minorities, study finds

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AI could help to tackle widespread disparities in the UK’s healthcare system if implemented properly, but it could actually widen health inequalities experienced by minority ethnic groups if not, researchers from Imperial College London (ICL) have said.

In a new paper, they provide several key recommendations including improving diversity in the AI industry and academia, and developing legislation and regulation to reduce bias in data and the systems that harness them.

While data-driven technologies like AI can be utilised as powerful tools to diagnose and treat diseases such as skin cancer, they could inadvertently worsen the health inequalities experienced by minority ethnic groups if current challenges such as “biased algorithms, poor data collection and a lack of diversity in research and development are not urgently addressed”, the paper said.

The findings were based on reviews of academic literature and policy evidence to identify the issues and opportunities for AI and data-driven technologies to improve the health and care of minority ethnic groups, who generally experience poorer health than the wider population, as emphasised by the Covid-19 pandemic.

The study included interviews with a range of experts in the UK and internationally across academia, industry, NHS policy and practice, legal and regulatory bodies, patient-facing organisations and charities.

Artificial intelligence systems are created by combining large amounts of data, for example from research studies or the internet.

The information is then used to ‘train’ a computer program or algorithm to make decisions based on the data. For example, using data, AI algorithms can create ‘risk scores’ to predict which patients might be likely to develop certain diseases in the future.

Yet if much of this data is unrepresentative of minority ethnic groups and focuses predominantly on white participants, for example, then these systems are more likely to make decisions which exclude diverse communities.

The report presents evidence of this racial bias in AI, demonstrating how minority ethnic groups can be underserved by technology.

For example, facial recognition systems have shown to be up to 19 per cent less accurate at recognising images of black men and women compared to white individuals. Such bias is also seen in AI when used in the detection and treatment of health conditions such as skin cancer.

Images of white patients are predominantly used to train algorithms to spot melanoma, which could lead to worse outcomes for black people through missed diagnoses.

The authors argue unconscious and conscious bias in AI is partly fuelled by the lack of diversity in academia, among AI developers and at strategic levels of the health system and government.

Experts interviewed for the report also voiced concern that the lack of diversity in the AI workforce could lead to solutions which are not fully representative of all users’ needs. It advises that improving representation in this industry must go further than addressing recruitment processes. Rather, the issue should also be tackled at all stages of education from primary school to postgraduate level.

Dr Saira Ghafur, digital health lead at ICL’s Institute of Global Health Innovation, said: “AI has tremendous potential for healthcare system delivery. However, our white paper shows how it can exacerbate existing health inequities in minority ethnic groups. By working across government, healthcare and the technology sector, it is crucial we ensure that no one is left behind.”

Lord James O’Shaughnessy, visiting professor at the Institute of Global Health Innovation, said: “Tackling health inequality is one of the major challenges of our time. Advances in AI and machine learning give us new tools to tackle this challenge, but our enthusiasm must be tempered by a realistic appraisal of the risks of these technologies inadvertently perpetuating inequalities.

“This paper explains how these risks could manifest and makes concrete proposals about how to mitigate them. Policymakers should heed the lessons of the report so that the wonderful advances in computer science can benefit those who most need it.”

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