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AI will soon be everywhere in the NHS. It's a risk for women and ethnic minorities, experts say

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Artificial intelligence (AI) could lead to UK health services that disadvantage women and ethnic minorities, scientists are warning. They are calling for biases in the systems to be rooted out before their use becomes commonplace in the NHS. They fear that without that preparation AI could dramatically deepen existing health inequalities in our society. The researchers examined the state of the art approach to AI used by hospitals worldwide and found it had a 70 per cent success rate in predicting liver disease from blood tests. But they uncovered a wide gender gap underneath – with 44 per cent of cases in women missed, compared with 23 per cent of cases among men.


Why artificial intelligence in the NHS could fail women and ethnic minorities

#artificialintelligence

Artificial intelligence (AI) could lead to UK health services that disadvantage women and ethnic minorities, scientists are warning. They are calling for biases in the systems to be rooted out before their use becomes commonplace in the NHS. They fear that without that preparation AI could dramatically deepen existing health inequalities in our society. The researchers examined the state of the art approach to AI used by hospitals worldwide and found it had a 70 per cent success rate in predicting liver disease from blood tests. But they uncovered a wide gender gap underneath – with 44 per cent of cases in women missed, compared with 23 per cent of cases among men.


How to prevent human bias from infecting AI

#artificialintelligence

Artificial intelligence is already an integral part of our lives. From Google Maps to Alexa, AI makes our lives more convenient. Less visibly, AI has helped streamline operations across a range of industries by automating mundane tasks. But as AI expands its applications, many have expressed concerns that it will exacerbate existing inequalities. Numbers from the World Economic Forum suggest that more than half of the 1.4 million US workers expected to be affected by tech disruption will be women. And there are questions about ways that algorithms reflect human biases.